In [2]:
%matplotlib inline
import pandas as pd
import numpy as np
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.stats.multicomp import MultiComparison
from statsmodels.formula.api import ols
from scipy import stats
In [3]:
data = pd.read_csv("playlists.csv", sep=";", encoding = "ISO-8859-1")
In [4]:
data.describe(include="all")
Out[4]:
| company | playlist_sample | namesfiles | no | artist | song | sampleratefiles | totalsamplesfiles | durationfiles | bitratefiles | ... | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | attackslopefiles | attackleapfiles | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1782 | 1782.000000 | 1782 | 1782.000000 | 1782 | 1782 | 1782.0 | 1.782000e+03 | 1782.000000 | 1782.000000 | ... | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 | 1782.000000 |
| unique | 6 | NaN | 515 | NaN | 353 | 443 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| top | Arte Francés | NaN | 21 - Galantis, Uffie - Spaceship (feat. Uffie)... | NaN | Satin Jackets | Hula Hoop.mp3 ... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| freq | 441 | NaN | 6 | NaN | 51 | 12 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| mean | NaN | 2.084175 | NaN | 17.116162 | NaN | NaN | 44100.0 | 1.043632e+07 | 236.651237 | 252.336700 | ... | 0.332301 | 0.319191 | 0.265246 | 0.440462 | 0.549565 | 0.581967 | 0.477825 | 0.430522 | 15.804409 | 0.507503 |
| std | NaN | 1.114796 | NaN | 11.837401 | NaN | NaN | 0.0 | 3.227105e+06 | 73.176981 | 88.377597 | ... | 0.270616 | 0.263919 | 0.249612 | 0.290454 | 0.314771 | 0.323173 | 0.321646 | 0.295563 | 9.338659 | 0.247587 |
| min | NaN | 1.000000 | NaN | 1.000000 | NaN | NaN | 44100.0 | 5.965054e+06 | 135.262000 | 128.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.168304 |
| 25% | NaN | 1.000000 | NaN | 8.000000 | NaN | NaN | 44100.0 | 8.353151e+06 | 189.413850 | 128.000000 | ... | 0.116591 | 0.109123 | 0.075378 | 0.204738 | 0.301961 | 0.316822 | 0.214680 | 0.203097 | 9.810711 | 0.285584 |
| 50% | NaN | 2.000000 | NaN | 15.500000 | NaN | NaN | 44100.0 | 9.480378e+06 | 214.974562 | 320.000000 | ... | 0.262799 | 0.259887 | 0.183082 | 0.396861 | 0.520957 | 0.570088 | 0.418077 | 0.379737 | 14.833864 | 0.452456 |
| 75% | NaN | 3.000000 | NaN | 24.000000 | NaN | NaN | 44100.0 | 1.146931e+07 | 260.075075 | 320.000000 | ... | 0.494897 | 0.469603 | 0.384485 | 0.642814 | 0.829318 | 0.918554 | 0.735384 | 0.616558 | 19.964413 | 0.730669 |
| max | NaN | 5.000000 | NaN | 65.000000 | NaN | NaN | 44100.0 | 2.843136e+07 | 644.702000 | 320.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 66.233620 | 0.999408 |
11 rows × 64 columns
Find positives and negatives songs of the process for every company.
In [5]:
companies = data['company'].unique()
by_company = [data[data.company == company] for company in companies]
positives = []
negatives = []
for data_com in by_company:
data_com = data_com.sort_values('playlist_sample')
last_pl = int(data_com.tail(1).playlist_sample)
pls = pd.DataFrame({'pl':range (1,last_pl+1), 'old':[0]*last_pl, 'new':[0]*last_pl})
# pls.new[0]=(data_com.query('playlist_sample == '+str(1)).shape[0])/3
# for i in range(2,last_pl+1):
# curr_pl = data_com.query('playlist_sample == '+str(i))
# pre_pl = data_com.query('playlist_sample == '+str(i-1))
# olds = curr_pl['song'].map(pre_pl['song'].value_counts()).sum(axis = 0)/3
# pls.old[i-1]= olds/3
# pls.new[i-1]=(curr_pl.shape[0]-olds)/3
# pls[['old','new']].plot(kind='bar', stacked=True, title=data_com.iloc[0,0])
df_last_pl= data_com.query('playlist_sample == '+str(last_pl))
positives.append(df_last_pl)
pos_loc = pd.DataFrame({}, columns=data_com.columns)
for index, row in data_com[data_com.playlist_sample<last_pl].iterrows():
if not ((df_last_pl['artist'] == row['artist']) & (df_last_pl['song'] == row['song'])).any():
pos_loc= pos_loc.append(row, ignore_index=True)
# n_vs_p = pd.DataFrame({'sam':['pos', 'neg'],'num':[df_last_pl.shape[0]/3,pos_loc.shape[0]/3]})
# n_vs_p.plot.bar(x='sam', y='num', rot=0, title=data_com.iloc[0,0])
negatives.append(pos_loc)
In [6]:
df_n_ps = []
for i in range(len(negatives)):
negatives[i]['chosen']=0
positives[i]['chosen']=1
df_n_ps.append(negatives[i].append(positives[i]))
D:\Usuarios\1144084318\AppData\Roaming\Python\Python37\site-packages\ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy after removing the cwd from sys.path.
ANOVA¶
In [7]:
import warnings
import matplotlib.pyplot as plt
import math
import seaborn as sns
warnings.filterwarnings('ignore')
In [ ]:
alpha = 0.05
for df_n_p in df_n_ps:
df_n_p = df_n_p.fillna(0)
fig = plt.figure(figsize=(17,200))
i=1
for index in range(8,df_n_p.shape[1]-1):
name= df_n_p.columns.values[index]
df_n_p[name]=df_n_p[name].astype('float64')
mc = MultiComparison(df_n_p[name], df_n_p['chosen'])
mc_results = mc.tukeyhsd()
if mc_results._results_table.data[1:][0][5]:
# print(name)
results = ols(name+' ~ C(chosen)', data=df_n_p).fit()
homogeneity_test = stats.levene(df_n_p[name][df_n_p['chosen'] == 0], df_n_p[name][df_n_p['chosen'] == 1])[1]
normality_test = stats.shapiro(results.resid)[1]
if homogeneity_test > alpha and normality_test > alpha:
# print(results.summary())
ax = fig.add_subplot(math.ceil(df_n_p.shape[1]-9/2), 2, i)
sns.kdeplot(df_n_p.loc[df_n_p.chosen==0][name], shade=True, ax=ax);
sns.kdeplot(df_n_p.loc[df_n_p.chosen==1][name], shade=True, ax=ax);
plt.title(df_n_p.iloc[0,0].upper()+" "+name)
plt.legend(['neg', 'pos'])
i+=1
In [8]:
from collections import Counter
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix, accuracy_score, silhouette_samples, silhouette_score, calinski_harabaz_score
from sklearn import preprocessing
from sklearn.decomposition import PCA
In [9]:
for i in range(len(companies)):
df_n_ps[i].bitratefiles = df_n_ps[i].bitratefiles.astype('float64')
df_n_ps[i].pitchfiles = df_n_ps[i].pitchfiles.astype('float64')
df_n_ps[i].bestkeyfiles = df_n_ps[i].bestkeyfiles.astype('float64')
df_n_ps[0].info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 372 entries, 0 to 179 Data columns (total 65 columns): company 372 non-null object playlist_sample 372 non-null object namesfiles 372 non-null object no 372 non-null object artist 372 non-null object song 372 non-null object sampleratefiles 372 non-null object totalsamplesfiles 372 non-null object durationfiles 372 non-null float64 bitratefiles 372 non-null float64 rmsfiles 372 non-null float64 rmsmedianfiles 372 non-null float64 lowenergyfiles 372 non-null float64 ASRfiles 372 non-null float64 beatspectrumfiles 372 non-null float64 eventdensityfiles 372 non-null float64 tempofiles 372 non-null float64 pulseclarityfiles 372 non-null float64 zerocrossfiles 372 non-null float64 rolloffsfiles 372 non-null float64 brightnessfiles 372 non-null float64 spreadfiles 372 non-null float64 centroidfiles 371 non-null float64 kurtosisfiles 372 non-null float64 flatnessfiles 372 non-null float64 entropyfiles 372 non-null float64 mfccfiles_1 372 non-null float64 mfccfiles_2 372 non-null float64 mfccfiles_3 372 non-null float64 mfccfiles_4 372 non-null float64 mfccfiles_5 372 non-null float64 mfccfiles_6 372 non-null float64 mfccfiles_7 372 non-null float64 mfccfiles_8 372 non-null float64 mfccfiles_9 372 non-null float64 mfccfiles_10 372 non-null float64 mfccfiles_11 372 non-null float64 mfccfiles_12 372 non-null float64 mfccfiles_13 372 non-null float64 pitchfiles 372 non-null float64 inharmonicityfiles 372 non-null float64 bestkeyfiles 372 non-null float64 keyclarityfiles 372 non-null float64 modalityfiles 372 non-null float64 tonalcentroidfiles_1 372 non-null float64 tonalcentroidfiles_2 372 non-null float64 tonalcentroidfiles_3 372 non-null float64 tonalcentroidfiles_4 372 non-null float64 tonalcentroidfiles_5 372 non-null float64 tonalcentroidfiles_6 372 non-null float64 chromagramfiles_1 372 non-null float64 chromagramfiles_2 372 non-null float64 chromagramfiles_3 372 non-null float64 chromagramfiles_4 372 non-null float64 chromagramfiles_5 372 non-null float64 chromagramfiles_6 372 non-null float64 chromagramfiles_7 372 non-null float64 chromagramfiles_8 372 non-null float64 chromagramfiles_9 372 non-null float64 chromagramfiles_10 372 non-null float64 chromagramfiles_11 372 non-null float64 chromagramfiles_12 372 non-null float64 attackslopefiles 372 non-null float64 attackleapfiles 372 non-null float64 chosen 372 non-null int64 dtypes: float64(56), int64(1), object(8) memory usage: 191.8+ KB
Vamos a reemplazar los NaN y entonces a normalizar los datos para que todas las variables tengan la misma importancia. Solo vamos a considerar los datos numéricos.
In [10]:
df_n_ps_std = [0]*len(companies)
for i in range(len(companies)):
df_n_ps[i] = df_n_ps[i].fillna(0)
df_n_ps_std[i] = pd.DataFrame(preprocessing.scale(df_n_ps[i].iloc[:,8:]))
df_n_ps_std[i].columns=df_n_ps[i].columns[8:]
df_n_ps_std[0].mean(axis=0)
Out[10]:
durationfiles -4.261824e-16 bitratefiles 0.000000e+00 rmsfiles 4.303606e-16 rmsmedianfiles -4.279731e-16 lowenergyfiles -2.387576e-18 ASRfiles -2.023471e-16 beatspectrumfiles 2.477111e-16 eventdensityfiles -7.879002e-17 tempofiles -3.133694e-17 pulseclarityfiles 3.103849e-17 zerocrossfiles -2.930750e-16 rolloffsfiles 5.789873e-16 brightnessfiles -8.356517e-17 spreadfiles -3.842506e-16 centroidfiles -2.142850e-16 kurtosisfiles -6.327077e-17 flatnessfiles 1.366887e-16 entropyfiles 3.516900e-15 mfccfiles_1 -1.921999e-16 mfccfiles_2 -5.372047e-18 mfccfiles_3 -1.178120e-16 mfccfiles_4 -2.648718e-17 mfccfiles_5 -4.655774e-17 mfccfiles_6 -1.193788e-18 mfccfiles_7 6.002516e-17 mfccfiles_8 1.492235e-17 mfccfiles_9 5.133289e-17 mfccfiles_10 2.596489e-17 mfccfiles_11 -3.402296e-17 mfccfiles_12 -4.775153e-18 mfccfiles_13 -4.476706e-18 pitchfiles 0.000000e+00 inharmonicityfiles 2.595743e-15 bestkeyfiles -8.475896e-17 keyclarityfiles 5.369062e-16 modalityfiles -3.282918e-17 tonalcentroidfiles_1 -1.522080e-17 tonalcentroidfiles_2 -6.565835e-18 tonalcentroidfiles_3 -9.699529e-18 tonalcentroidfiles_4 1.671303e-17 tonalcentroidfiles_5 -2.193586e-17 tonalcentroidfiles_6 2.059285e-17 chromagramfiles_1 -1.811574e-16 chromagramfiles_2 -4.282715e-17 chromagramfiles_3 4.819920e-17 chromagramfiles_4 -2.188363e-16 chromagramfiles_5 -3.282918e-18 chromagramfiles_6 -8.834033e-17 chromagramfiles_7 3.730588e-17 chromagramfiles_8 -1.140068e-16 chromagramfiles_9 -2.715868e-17 chromagramfiles_10 -6.707597e-17 chromagramfiles_11 -6.051014e-17 chromagramfiles_12 2.148446e-16 attackslopefiles -4.327482e-17 attackleapfiles -1.551925e-16 chosen -3.068036e-16 dtype: float64
In [11]:
df_n_ps_std[0].std(axis=0)
Out[11]:
durationfiles 1.001347 bitratefiles 0.000000 rmsfiles 1.001347 rmsmedianfiles 1.001347 lowenergyfiles 1.001347 ASRfiles 1.001347 beatspectrumfiles 1.001347 eventdensityfiles 1.001347 tempofiles 1.001347 pulseclarityfiles 1.001347 zerocrossfiles 1.001347 rolloffsfiles 1.001347 brightnessfiles 1.001347 spreadfiles 1.001347 centroidfiles 1.001347 kurtosisfiles 1.001347 flatnessfiles 1.001347 entropyfiles 1.001347 mfccfiles_1 1.001347 mfccfiles_2 1.001347 mfccfiles_3 1.001347 mfccfiles_4 1.001347 mfccfiles_5 1.001347 mfccfiles_6 1.001347 mfccfiles_7 1.001347 mfccfiles_8 1.001347 mfccfiles_9 1.001347 mfccfiles_10 1.001347 mfccfiles_11 1.001347 mfccfiles_12 1.001347 mfccfiles_13 1.001347 pitchfiles 0.000000 inharmonicityfiles 1.001347 bestkeyfiles 1.001347 keyclarityfiles 1.001347 modalityfiles 1.001347 tonalcentroidfiles_1 1.001347 tonalcentroidfiles_2 1.001347 tonalcentroidfiles_3 1.001347 tonalcentroidfiles_4 1.001347 tonalcentroidfiles_5 1.001347 tonalcentroidfiles_6 1.001347 chromagramfiles_1 1.001347 chromagramfiles_2 1.001347 chromagramfiles_3 1.001347 chromagramfiles_4 1.001347 chromagramfiles_5 1.001347 chromagramfiles_6 1.001347 chromagramfiles_7 1.001347 chromagramfiles_8 1.001347 chromagramfiles_9 1.001347 chromagramfiles_10 1.001347 chromagramfiles_11 1.001347 chromagramfiles_12 1.001347 attackslopefiles 1.001347 attackleapfiles 1.001347 chosen 1.001347 dtype: float64
Borramos pitch y bitrate porque todos sus valores son 0.
In [12]:
for i in range(len(companies)):
df_n_ps_std[i] = df_n_ps_std[i].drop(columns="pitchfiles")
df_n_ps_std[i] = df_n_ps_std[i].drop(columns="bitratefiles")
In [16]:
df_n_ps_std[0].columns
Out[16]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
MFCC¶
In [175]:
df_n_ps_std[0].columns[17:30]
Out[175]:
Index(['mfccfiles_1', 'mfccfiles_2', 'mfccfiles_3', 'mfccfiles_4',
'mfccfiles_5', 'mfccfiles_6', 'mfccfiles_7', 'mfccfiles_8',
'mfccfiles_9', 'mfccfiles_10', 'mfccfiles_11', 'mfccfiles_12',
'mfccfiles_13'],
dtype='object')
In [176]:
df_n_ps_std_mfcc = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_mfcc[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,17:30])
df_n_ps_std_mfcc[i].columns=df_n_ps_std[i].columns[17:30]
df_n_ps_std_mfcc[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 372 entries, 0 to 371 Data columns (total 13 columns): mfccfiles_1 372 non-null float64 mfccfiles_2 372 non-null float64 mfccfiles_3 372 non-null float64 mfccfiles_4 372 non-null float64 mfccfiles_5 372 non-null float64 mfccfiles_6 372 non-null float64 mfccfiles_7 372 non-null float64 mfccfiles_8 372 non-null float64 mfccfiles_9 372 non-null float64 mfccfiles_10 372 non-null float64 mfccfiles_11 372 non-null float64 mfccfiles_12 372 non-null float64 mfccfiles_13 372 non-null float64 dtypes: float64(13) memory usage: 37.9 KB
Arte Francés¶
ANN¶
In [13]:
import keras
keras.__version__
Using TensorFlow backend.
Out[13]:
'2.3.0'
In [14]:
from keras.layers import Input, Flatten, Dense#, Lambda
from keras.models import Model
from keras import layers
from keras import models, optimizers
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV #permite buscar la mejor configuración de parámetros con C-V
from sklearn.metrics import make_scorer # permite crear una clase scorer a partir de una función de score (necesario para el kappa)
from sklearn.metrics import accuracy_score, cohen_kappa_score, classification_report, roc_auc_score
from sklearn.model_selection import train_test_split #metodo de particionamiento de datasets para evaluación
from sklearn.preprocessing import StandardScaler
In [177]:
X = df_n_ps_std_mfcc[0]
In [178]:
y = df_n_ps[0]['chosen']
In [179]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [180]:
X_train.shape
Out[180]:
(279, 13)
In [24]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [25]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [26]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [27]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.006, 'max_iter': 300}, que permiten obtener un Accuracy de 82.08% y un Kappa del 43.49
Tiempo total: 29.12 minutos
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. % self.max_iter, ConvergenceWarning)
In [181]:
grid.best_params_['hidden_layer_sizes']=[20]
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.006
epochs = 300
In [182]:
input_tensor = Input(shape = (n0,))
In [183]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [184]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [185]:
model.summary()
Model: "model_6" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_6 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_17 (Dense) (None, 20) 280 _________________________________________________________________ dense_18 (Dense) (None, 1) 21 ================================================================= Total params: 301 Trainable params: 301 Non-trainable params: 0 _________________________________________________________________
In [186]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 279 samples, validate on 93 samples Epoch 1/300 279/279 [==============================] - 0s 641us/step - loss: 0.7161 - accuracy: 0.5556 - val_loss: 0.6348 - val_accuracy: 0.6667 Epoch 2/300 279/279 [==============================] - 0s 47us/step - loss: 0.5940 - accuracy: 0.7025 - val_loss: 0.6026 - val_accuracy: 0.6989 Epoch 3/300 279/279 [==============================] - 0s 47us/step - loss: 0.5317 - accuracy: 0.7599 - val_loss: 0.5880 - val_accuracy: 0.7097 Epoch 4/300 279/279 [==============================] - 0s 47us/step - loss: 0.4879 - accuracy: 0.7849 - val_loss: 0.5798 - val_accuracy: 0.7097 Epoch 5/300 279/279 [==============================] - 0s 50us/step - loss: 0.4620 - accuracy: 0.7957 - val_loss: 0.5636 - val_accuracy: 0.7312 Epoch 6/300 279/279 [==============================] - 0s 50us/step - loss: 0.4473 - accuracy: 0.7993 - val_loss: 0.5569 - val_accuracy: 0.7204 Epoch 7/300 279/279 [==============================] - 0s 79us/step - loss: 0.4387 - accuracy: 0.7993 - val_loss: 0.5600 - val_accuracy: 0.7312 Epoch 8/300 279/279 [==============================] - 0s 54us/step - loss: 0.4304 - accuracy: 0.8065 - val_loss: 0.5599 - val_accuracy: 0.7419 Epoch 9/300 279/279 [==============================] - 0s 47us/step - loss: 0.4272 - accuracy: 0.7993 - val_loss: 0.5650 - val_accuracy: 0.7204 Epoch 10/300 279/279 [==============================] - 0s 54us/step - loss: 0.4187 - accuracy: 0.8100 - val_loss: 0.5698 - val_accuracy: 0.7312 Epoch 11/300 279/279 [==============================] - 0s 54us/step - loss: 0.4159 - accuracy: 0.8172 - val_loss: 0.5727 - val_accuracy: 0.7312 Epoch 12/300 279/279 [==============================] - 0s 50us/step - loss: 0.4103 - accuracy: 0.8244 - val_loss: 0.5749 - val_accuracy: 0.7312 Epoch 13/300 279/279 [==============================] - 0s 54us/step - loss: 0.4058 - accuracy: 0.8315 - val_loss: 0.5743 - val_accuracy: 0.6989 Epoch 14/300 279/279 [==============================] - 0s 54us/step - loss: 0.4027 - accuracy: 0.8315 - val_loss: 0.5734 - val_accuracy: 0.7312 Epoch 15/300 279/279 [==============================] - 0s 72us/step - loss: 0.3979 - accuracy: 0.8315 - val_loss: 0.5791 - val_accuracy: 0.7527 Epoch 16/300 279/279 [==============================] - 0s 64us/step - loss: 0.3922 - accuracy: 0.8315 - val_loss: 0.5729 - val_accuracy: 0.7097 Epoch 17/300 279/279 [==============================] - 0s 57us/step - loss: 0.3870 - accuracy: 0.8423 - val_loss: 0.5687 - val_accuracy: 0.7204 Epoch 18/300 279/279 [==============================] - 0s 50us/step - loss: 0.3831 - accuracy: 0.8423 - val_loss: 0.5750 - val_accuracy: 0.7312 Epoch 19/300 279/279 [==============================] - 0s 57us/step - loss: 0.3779 - accuracy: 0.8387 - val_loss: 0.5681 - val_accuracy: 0.7204 Epoch 20/300 279/279 [==============================] - 0s 57us/step - loss: 0.3730 - accuracy: 0.8423 - val_loss: 0.5734 - val_accuracy: 0.7204 Epoch 21/300 279/279 [==============================] - 0s 57us/step - loss: 0.3681 - accuracy: 0.8423 - val_loss: 0.5783 - val_accuracy: 0.7097 Epoch 22/300 279/279 [==============================] - 0s 57us/step - loss: 0.3633 - accuracy: 0.8423 - val_loss: 0.5685 - val_accuracy: 0.7312 Epoch 23/300 279/279 [==============================] - 0s 57us/step - loss: 0.3576 - accuracy: 0.8459 - val_loss: 0.5778 - val_accuracy: 0.7312 Epoch 24/300 279/279 [==============================] - 0s 82us/step - loss: 0.3528 - accuracy: 0.8459 - val_loss: 0.5799 - val_accuracy: 0.7097 Epoch 25/300 279/279 [==============================] - 0s 64us/step - loss: 0.3481 - accuracy: 0.8495 - val_loss: 0.5794 - val_accuracy: 0.7312 Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.003000000026077032. Epoch 26/300 279/279 [==============================] - 0s 64us/step - loss: 0.3408 - accuracy: 0.8530 - val_loss: 0.5796 - val_accuracy: 0.7204 Epoch 27/300 279/279 [==============================] - 0s 50us/step - loss: 0.3393 - accuracy: 0.8530 - val_loss: 0.5772 - val_accuracy: 0.7097 Epoch 28/300 279/279 [==============================] - 0s 50us/step - loss: 0.3361 - accuracy: 0.8530 - val_loss: 0.5776 - val_accuracy: 0.7097 Epoch 29/300 279/279 [==============================] - 0s 50us/step - loss: 0.3335 - accuracy: 0.8530 - val_loss: 0.5781 - val_accuracy: 0.7204 Epoch 30/300 279/279 [==============================] - 0s 54us/step - loss: 0.3306 - accuracy: 0.8566 - val_loss: 0.5785 - val_accuracy: 0.7097 Epoch 31/300 279/279 [==============================] - 0s 50us/step - loss: 0.3282 - accuracy: 0.8566 - val_loss: 0.5786 - val_accuracy: 0.7097 Epoch 32/300 279/279 [==============================] - 0s 50us/step - loss: 0.3252 - accuracy: 0.8602 - val_loss: 0.5801 - val_accuracy: 0.7097 Epoch 33/300 279/279 [==============================] - 0s 50us/step - loss: 0.3228 - accuracy: 0.8602 - val_loss: 0.5783 - val_accuracy: 0.7204 Epoch 34/300 279/279 [==============================] - 0s 50us/step - loss: 0.3201 - accuracy: 0.8602 - val_loss: 0.5823 - val_accuracy: 0.6989 Epoch 35/300 279/279 [==============================] - 0s 68us/step - loss: 0.3176 - accuracy: 0.8602 - val_loss: 0.5773 - val_accuracy: 0.7097 Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 36/300 279/279 [==============================] - 0s 57us/step - loss: 0.3139 - accuracy: 0.8674 - val_loss: 0.5800 - val_accuracy: 0.7097 Epoch 37/300 279/279 [==============================] - 0s 50us/step - loss: 0.3130 - accuracy: 0.8674 - val_loss: 0.5834 - val_accuracy: 0.6989 Epoch 38/300 279/279 [==============================] - 0s 54us/step - loss: 0.3114 - accuracy: 0.8638 - val_loss: 0.5815 - val_accuracy: 0.7097 Epoch 39/300 279/279 [==============================] - 0s 61us/step - loss: 0.3097 - accuracy: 0.8638 - val_loss: 0.5837 - val_accuracy: 0.6989 Epoch 40/300 279/279 [==============================] - 0s 50us/step - loss: 0.3086 - accuracy: 0.8638 - val_loss: 0.5840 - val_accuracy: 0.7097 Epoch 41/300 279/279 [==============================] - 0s 50us/step - loss: 0.3073 - accuracy: 0.8674 - val_loss: 0.5840 - val_accuracy: 0.6989 Epoch 42/300 279/279 [==============================] - 0s 54us/step - loss: 0.3053 - accuracy: 0.8674 - val_loss: 0.5843 - val_accuracy: 0.6989 Epoch 43/300 279/279 [==============================] - 0s 50us/step - loss: 0.3051 - accuracy: 0.8674 - val_loss: 0.5836 - val_accuracy: 0.7097 Epoch 44/300 279/279 [==============================] - 0s 54us/step - loss: 0.3031 - accuracy: 0.8674 - val_loss: 0.5828 - val_accuracy: 0.7097 Epoch 45/300 279/279 [==============================] - 0s 50us/step - loss: 0.3014 - accuracy: 0.8638 - val_loss: 0.5844 - val_accuracy: 0.7097 Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 46/300 279/279 [==============================] - 0s 54us/step - loss: 0.3004 - accuracy: 0.8638 - val_loss: 0.5851 - val_accuracy: 0.7097 Epoch 47/300 279/279 [==============================] - 0s 54us/step - loss: 0.2993 - accuracy: 0.8638 - val_loss: 0.5854 - val_accuracy: 0.7097 Epoch 48/300 279/279 [==============================] - 0s 50us/step - loss: 0.2988 - accuracy: 0.8638 - val_loss: 0.5866 - val_accuracy: 0.7097 Epoch 49/300 279/279 [==============================] - 0s 50us/step - loss: 0.2981 - accuracy: 0.8674 - val_loss: 0.5869 - val_accuracy: 0.7097 Epoch 50/300 279/279 [==============================] - 0s 50us/step - loss: 0.2973 - accuracy: 0.8674 - val_loss: 0.5866 - val_accuracy: 0.7097 Epoch 51/300 279/279 [==============================] - 0s 54us/step - loss: 0.2967 - accuracy: 0.8674 - val_loss: 0.5879 - val_accuracy: 0.7097 Epoch 52/300 279/279 [==============================] - 0s 50us/step - loss: 0.2961 - accuracy: 0.8674 - val_loss: 0.5873 - val_accuracy: 0.7097 Epoch 53/300 279/279 [==============================] - 0s 50us/step - loss: 0.2953 - accuracy: 0.8674 - val_loss: 0.5886 - val_accuracy: 0.7097 Epoch 54/300 279/279 [==============================] - 0s 50us/step - loss: 0.2947 - accuracy: 0.8710 - val_loss: 0.5880 - val_accuracy: 0.7097 Epoch 55/300 279/279 [==============================] - 0s 54us/step - loss: 0.2941 - accuracy: 0.8674 - val_loss: 0.5889 - val_accuracy: 0.7097 Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 56/300 279/279 [==============================] - 0s 54us/step - loss: 0.2933 - accuracy: 0.8710 - val_loss: 0.5887 - val_accuracy: 0.7097 Epoch 57/300 279/279 [==============================] - 0s 75us/step - loss: 0.2931 - accuracy: 0.8746 - val_loss: 0.5885 - val_accuracy: 0.7097 Epoch 58/300 279/279 [==============================] - 0s 64us/step - loss: 0.2927 - accuracy: 0.8746 - val_loss: 0.5887 - val_accuracy: 0.7097 Epoch 59/300 279/279 [==============================] - 0s 61us/step - loss: 0.2923 - accuracy: 0.8781 - val_loss: 0.5899 - val_accuracy: 0.7097 Epoch 60/300 279/279 [==============================] - 0s 54us/step - loss: 0.2919 - accuracy: 0.8781 - val_loss: 0.5899 - val_accuracy: 0.7097 Epoch 61/300 279/279 [==============================] - 0s 57us/step - loss: 0.2916 - accuracy: 0.8781 - val_loss: 0.5900 - val_accuracy: 0.7097 Epoch 62/300 279/279 [==============================] - 0s 57us/step - loss: 0.2913 - accuracy: 0.8781 - val_loss: 0.5906 - val_accuracy: 0.7097 Epoch 63/300 279/279 [==============================] - 0s 50us/step - loss: 0.2909 - accuracy: 0.8781 - val_loss: 0.5904 - val_accuracy: 0.7097 Epoch 64/300 279/279 [==============================] - 0s 54us/step - loss: 0.2907 - accuracy: 0.8781 - val_loss: 0.5902 - val_accuracy: 0.7097 Epoch 65/300 279/279 [==============================] - 0s 50us/step - loss: 0.2902 - accuracy: 0.8781 - val_loss: 0.5903 - val_accuracy: 0.7097 Epoch 00065: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 66/300 279/279 [==============================] - 0s 61us/step - loss: 0.2898 - accuracy: 0.8781 - val_loss: 0.5902 - val_accuracy: 0.7097 Epoch 67/300 279/279 [==============================] - 0s 54us/step - loss: 0.2897 - accuracy: 0.8781 - val_loss: 0.5903 - val_accuracy: 0.7097 Epoch 68/300 279/279 [==============================] - 0s 50us/step - loss: 0.2895 - accuracy: 0.8781 - val_loss: 0.5903 - val_accuracy: 0.7097 Epoch 69/300 279/279 [==============================] - 0s 54us/step - loss: 0.2894 - accuracy: 0.8781 - val_loss: 0.5905 - val_accuracy: 0.7097 Epoch 70/300 279/279 [==============================] - 0s 54us/step - loss: 0.2892 - accuracy: 0.8781 - val_loss: 0.5905 - val_accuracy: 0.7097 Epoch 71/300 279/279 [==============================] - 0s 54us/step - loss: 0.2890 - accuracy: 0.8781 - val_loss: 0.5906 - val_accuracy: 0.7097 Epoch 72/300 279/279 [==============================] - 0s 75us/step - loss: 0.2889 - accuracy: 0.8781 - val_loss: 0.5906 - val_accuracy: 0.7097 Epoch 73/300 279/279 [==============================] - 0s 57us/step - loss: 0.2887 - accuracy: 0.8781 - val_loss: 0.5904 - val_accuracy: 0.7097 Epoch 74/300 279/279 [==============================] - 0s 54us/step - loss: 0.2885 - accuracy: 0.8781 - val_loss: 0.5906 - val_accuracy: 0.7097 Epoch 75/300 279/279 [==============================] - 0s 54us/step - loss: 0.2884 - accuracy: 0.8781 - val_loss: 0.5905 - val_accuracy: 0.7097 Epoch 00075: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 76/300 279/279 [==============================] - 0s 50us/step - loss: 0.2882 - accuracy: 0.8781 - val_loss: 0.5907 - val_accuracy: 0.7097 Epoch 77/300 279/279 [==============================] - 0s 54us/step - loss: 0.2881 - accuracy: 0.8781 - val_loss: 0.5908 - val_accuracy: 0.7097 Epoch 78/300 279/279 [==============================] - 0s 54us/step - loss: 0.2880 - accuracy: 0.8781 - val_loss: 0.5908 - val_accuracy: 0.7097 Epoch 79/300 279/279 [==============================] - 0s 54us/step - loss: 0.2879 - accuracy: 0.8817 - val_loss: 0.5909 - val_accuracy: 0.7097 Epoch 80/300 279/279 [==============================] - 0s 57us/step - loss: 0.2879 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 81/300 279/279 [==============================] - 0s 79us/step - loss: 0.2878 - accuracy: 0.8817 - val_loss: 0.5909 - val_accuracy: 0.7097 Epoch 82/300 279/279 [==============================] - 0s 68us/step - loss: 0.2877 - accuracy: 0.8817 - val_loss: 0.5909 - val_accuracy: 0.7097 Epoch 83/300 279/279 [==============================] - 0s 54us/step - loss: 0.2876 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 84/300 279/279 [==============================] - 0s 57us/step - loss: 0.2875 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 85/300 279/279 [==============================] - 0s 72us/step - loss: 0.2874 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 00085: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 86/300 279/279 [==============================] - 0s 64us/step - loss: 0.2873 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 87/300 279/279 [==============================] - 0s 57us/step - loss: 0.2873 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 88/300 279/279 [==============================] - 0s 54us/step - loss: 0.2872 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 89/300 279/279 [==============================] - 0s 50us/step - loss: 0.2872 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 90/300 279/279 [==============================] - 0s 54us/step - loss: 0.2872 - accuracy: 0.8817 - val_loss: 0.5910 - val_accuracy: 0.7097 Epoch 91/300 279/279 [==============================] - 0s 50us/step - loss: 0.2871 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 92/300 279/279 [==============================] - 0s 50us/step - loss: 0.2871 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 93/300 279/279 [==============================] - 0s 54us/step - loss: 0.2870 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 94/300 279/279 [==============================] - 0s 50us/step - loss: 0.2870 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 95/300 279/279 [==============================] - 0s 54us/step - loss: 0.2869 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 00095: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 96/300 279/279 [==============================] - 0s 50us/step - loss: 0.2869 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 97/300 279/279 [==============================] - 0s 54us/step - loss: 0.2869 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 98/300 279/279 [==============================] - 0s 54us/step - loss: 0.2868 - accuracy: 0.8817 - val_loss: 0.5911 - val_accuracy: 0.7097 Epoch 99/300 279/279 [==============================] - 0s 50us/step - loss: 0.2868 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 100/300 279/279 [==============================] - 0s 47us/step - loss: 0.2868 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 101/300 279/279 [==============================] - 0s 54us/step - loss: 0.2868 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 102/300 279/279 [==============================] - 0s 54us/step - loss: 0.2868 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 103/300 279/279 [==============================] - 0s 54us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 104/300 279/279 [==============================] - 0s 54us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 105/300 279/279 [==============================] - 0s 50us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 00105: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 106/300 279/279 [==============================] - 0s 50us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 107/300 279/279 [==============================] - 0s 47us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 108/300 279/279 [==============================] - 0s 47us/step - loss: 0.2867 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 109/300 279/279 [==============================] - 0s 54us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 110/300 279/279 [==============================] - 0s 50us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 111/300 279/279 [==============================] - 0s 50us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 112/300 279/279 [==============================] - 0s 75us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 113/300 279/279 [==============================] - 0s 57us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 114/300 279/279 [==============================] - 0s 57us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 115/300 279/279 [==============================] - 0s 50us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5912 - val_accuracy: 0.7097 Epoch 00115: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 116/300 279/279 [==============================] - 0s 57us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 117/300 279/279 [==============================] - 0s 50us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 118/300 279/279 [==============================] - 0s 54us/step - loss: 0.2866 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 119/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 120/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 121/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 122/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 123/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 124/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 125/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00125: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 126/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 127/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 128/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 129/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 130/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 131/300 279/279 [==============================] - ETA: 0s - loss: 0.1649 - accuracy: 0.96 - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 132/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 133/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 134/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 135/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00135: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 136/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 137/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 138/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 139/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 140/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 141/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 142/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 143/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 144/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 145/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00145: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 146/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 147/300 279/279 [==============================] - 0s 79us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 148/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 149/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 150/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 151/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 152/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 153/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 154/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 155/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00155: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 156/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 157/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 158/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 159/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 160/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 161/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 162/300 279/279 [==============================] - 0s 82us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 163/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 164/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 165/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00165: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 166/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 167/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 168/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 169/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 170/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 171/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 172/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 173/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 174/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 175/300 279/279 [==============================] - 0s 79us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00175: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 176/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 177/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 178/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 179/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 180/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 181/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 182/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 183/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 184/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 185/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00185: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 186/300 279/279 [==============================] - 0s 75us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 187/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 188/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 189/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 190/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 191/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 192/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 193/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 194/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 195/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00195: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 196/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 197/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 198/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 199/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 200/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 201/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 202/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 203/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 204/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 205/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00205: ReduceLROnPlateau reducing learning rate to 1.1444091896350983e-08. Epoch 206/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 207/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 208/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 209/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 210/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 211/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 212/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 213/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 214/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 215/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00215: ReduceLROnPlateau reducing learning rate to 5.7220459481754915e-09. Epoch 216/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 217/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 218/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 219/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 220/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 221/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 222/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 223/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 224/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 225/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00225: ReduceLROnPlateau reducing learning rate to 2.8610229740877458e-09. Epoch 226/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 227/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 228/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 229/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 230/300 279/279 [==============================] - 0s 75us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 231/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 232/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 233/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 234/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 235/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00235: ReduceLROnPlateau reducing learning rate to 1.4305114870438729e-09. Epoch 236/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 237/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 238/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 239/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 240/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 241/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 242/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 243/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 244/300 279/279 [==============================] - 0s 86us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 245/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00245: ReduceLROnPlateau reducing learning rate to 7.152557435219364e-10. Epoch 246/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 247/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 248/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 249/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 250/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 251/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 252/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 253/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 254/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 255/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00255: ReduceLROnPlateau reducing learning rate to 3.576278717609682e-10. Epoch 256/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 257/300 279/279 [==============================] - 0s 47us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 258/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 259/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 260/300 279/279 [==============================] - 0s 90us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 261/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 262/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 263/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 264/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 265/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00265: ReduceLROnPlateau reducing learning rate to 1.788139358804841e-10. Epoch 266/300 279/279 [==============================] - ETA: 0s - loss: 0.3475 - accuracy: 0.81 - 0s 75us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 267/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 268/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 269/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 270/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 271/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 272/300 279/279 [==============================] - 0s 47us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 273/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 274/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 275/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00275: ReduceLROnPlateau reducing learning rate to 8.940696794024205e-11. Epoch 276/300 279/279 [==============================] - 0s 47us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 277/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 278/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 279/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 280/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 281/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 282/300 279/279 [==============================] - 0s 54us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 283/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 284/300 279/279 [==============================] - 0s 50us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 285/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00285: ReduceLROnPlateau reducing learning rate to 4.470348397012103e-11. Epoch 286/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 287/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 288/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 289/300 279/279 [==============================] - 0s 86us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 290/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 291/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 292/300 279/279 [==============================] - 0s 68us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 293/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 294/300 279/279 [==============================] - 0s 57us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 295/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 00295: ReduceLROnPlateau reducing learning rate to 2.2351741985060514e-11. Epoch 296/300 279/279 [==============================] - 0s 64us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 297/300 279/279 [==============================] - 0s 75us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 298/300 279/279 [==============================] - 0s 72us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 299/300 279/279 [==============================] - 0s 61us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097 Epoch 300/300 279/279 [==============================] - 0s 82us/step - loss: 0.2865 - accuracy: 0.8817 - val_loss: 0.5913 - val_accuracy: 0.7097
In [187]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 300)
In [188]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
93/93 [==============================] - 0s 43us/step test loss: 0.5912757192247657, test accuracy: 0.7096773982048035
In [190]:
y_pred = model.predict(X_test)
y_pred_d = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred_d))
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
Kappa: 0.17860647693817455 AUC ROC: 0.6812570145903478
KMeans¶
In [191]:
X
Out[191]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.303210 | 1.253016 | -0.394054 | 0.592598 | 0.106623 | -2.083256 | 0.858313 | 0.801936 | 2.380580 | -1.304258 | 0.556361 | 1.949530 | -1.046692 |
| 1 | 0.647559 | -1.553511 | -1.648243 | -1.010792 | -0.857927 | 0.335856 | 0.884468 | 0.250703 | 0.298648 | 0.683922 | 1.599907 | 0.349480 | -0.484103 |
| 2 | 2.266625 | -0.333664 | 0.685765 | -2.001143 | -0.820018 | -2.442242 | -1.583451 | -2.793213 | -2.158376 | -2.431307 | -0.855856 | -0.471626 | -1.478884 |
| 3 | 0.672266 | -0.397422 | 0.105106 | -1.822060 | -1.335294 | -1.384110 | -0.608579 | -1.639581 | -2.081721 | -1.171877 | -1.102493 | -1.264328 | -1.165166 |
| 4 | 0.747622 | 0.110331 | -0.079109 | -1.108698 | -0.391749 | -0.448919 | 0.133859 | -0.843237 | -1.924086 | -0.222835 | 0.221819 | 0.017631 | -0.725177 |
| 5 | 2.072441 | -1.565884 | -0.268750 | -1.648648 | -3.149219 | -3.406801 | -4.332461 | -1.709990 | -1.313696 | -1.503431 | -2.036749 | -1.928213 | -2.657430 |
| 6 | -0.163654 | 0.470736 | 0.440011 | 0.594090 | -1.227236 | 0.409218 | 0.608496 | -0.948833 | -2.051031 | 0.892327 | 0.371683 | -0.318984 | 0.022251 |
| 7 | 0.477732 | -0.217651 | -0.908178 | -1.257961 | -1.360625 | -0.551388 | -0.599896 | 0.099066 | 1.299780 | 2.443060 | 0.417236 | -0.919898 | -0.916391 |
| 8 | 0.897786 | -3.040013 | 0.311694 | -0.386220 | -0.321124 | -0.221380 | -0.207002 | -0.127210 | 0.011073 | 0.211925 | -0.400748 | -0.296623 | -0.143419 |
| 9 | -0.977087 | 1.088438 | -0.184899 | -0.626934 | 0.577247 | 0.522552 | 1.150101 | 1.023214 | 0.136257 | 0.193237 | -0.496760 | 0.367549 | 0.378679 |
| 10 | 0.328615 | -1.292300 | -1.398337 | -0.677268 | -1.070980 | -0.075073 | -0.740061 | -0.424240 | -0.216693 | 0.633892 | -0.070397 | 0.960392 | 0.403827 |
| 11 | -0.199470 | 0.110219 | 0.238637 | 0.455154 | -0.116209 | 0.374450 | 0.078145 | 0.424005 | 0.633052 | -0.153498 | -0.647002 | 0.301135 | -0.000406 |
| 12 | -0.711256 | 0.124802 | 0.734425 | -0.445078 | -0.503247 | -0.323539 | 0.236246 | -0.572803 | -0.221112 | -0.206486 | -0.180516 | 0.119335 | 0.027470 |
| 13 | -0.806898 | -0.126740 | -0.383726 | 0.035489 | -1.164460 | -0.574335 | -0.633858 | -0.009812 | -0.131411 | 0.549197 | -0.257952 | 0.307916 | 0.814674 |
| 14 | -0.077242 | -0.331495 | 0.550493 | -0.008575 | -0.215759 | -1.260552 | -0.581296 | 0.369790 | -0.684267 | 0.792489 | -0.457321 | -0.704205 | -0.093986 |
| 15 | 0.244538 | 0.777957 | 0.464181 | 0.169574 | -0.433604 | -1.172185 | -1.866928 | 0.759778 | -0.372608 | 0.009766 | 0.964104 | 1.082661 | -0.506505 |
| 16 | 0.602329 | -0.035069 | 0.178352 | -0.036690 | 0.180302 | -0.769568 | 0.364535 | 0.996915 | 0.263984 | -0.829872 | -0.133422 | 0.601135 | -1.217336 |
| 17 | -0.570258 | -0.759570 | 0.108993 | 0.657477 | 0.342355 | -0.903388 | 0.112467 | -0.669060 | -0.661619 | 0.915675 | 1.620722 | -0.160697 | 0.379275 |
| 18 | -0.288268 | -1.202534 | -0.544058 | 0.295908 | 0.568680 | -1.416228 | 0.423676 | 0.041836 | -0.665694 | 0.699155 | -0.070704 | -0.429451 | 1.194384 |
| 19 | -0.497305 | -0.552590 | 0.332470 | 0.660607 | 0.293725 | -0.945647 | -1.269354 | 0.464095 | 1.166255 | 2.034233 | 2.037855 | 0.555927 | 0.423683 |
| 20 | 1.386141 | -0.516432 | -0.074640 | 0.751101 | -1.151864 | 0.155819 | -1.921431 | -3.381158 | -1.145758 | -1.197084 | 0.654749 | 1.636425 | 0.993236 |
| 21 | 0.076772 | 0.072900 | 0.122544 | 0.799017 | -1.121011 | -0.137599 | -1.150187 | -1.669293 | -1.110882 | -0.047217 | -0.034112 | -0.659214 | 1.160642 |
| 22 | 0.670757 | -0.167252 | -0.352765 | 0.189499 | -1.232602 | -0.168579 | -1.559900 | -1.850665 | -1.416478 | 0.031846 | 0.308193 | -0.956133 | 0.507231 |
| 23 | 0.639283 | 0.699164 | 0.621380 | -0.725771 | -0.890352 | 0.643955 | -1.097228 | 0.229756 | -0.091793 | -2.390193 | -0.825768 | -2.164531 | -0.772983 |
| 24 | -0.907399 | 2.155157 | 0.873522 | 1.655111 | 0.871099 | 1.083262 | -0.186962 | 0.373227 | -0.354082 | 0.573586 | 0.733097 | -0.986481 | 0.727511 |
| 25 | -1.152272 | 1.601470 | 0.221927 | 1.296592 | 0.572807 | 0.581774 | -0.479257 | 0.209504 | -0.636178 | 0.574450 | 0.615706 | -0.877894 | 0.941827 |
| 26 | -0.676596 | 0.405600 | 0.553370 | 0.691531 | -0.292469 | 0.626694 | 0.080413 | 0.246868 | -0.100975 | 0.606694 | -0.024154 | -1.553730 | -0.210884 |
| 27 | -0.704834 | -0.058170 | 0.609171 | -0.735340 | -0.512747 | 0.796018 | -0.405976 | 0.502120 | 0.717380 | -1.625431 | 0.825742 | -1.663942 | -0.379395 |
| 28 | 0.273095 | 1.014503 | -0.772750 | -0.065028 | -0.513485 | 0.235377 | -0.266144 | 1.373964 | 0.711880 | -1.261758 | 1.106463 | 0.515863 | 0.555866 |
| 29 | 1.216372 | 0.637021 | 0.649194 | 0.099873 | -0.816614 | 0.555439 | -1.272918 | -0.035862 | 0.154194 | -1.797465 | -0.177830 | -1.702118 | -1.136716 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 342 | 0.056741 | 0.169776 | 0.434163 | -0.208821 | 0.498957 | 0.349198 | 0.547068 | 0.250228 | -0.743894 | -0.497075 | 0.373497 | -0.025547 | 0.260645 |
| 343 | -0.845812 | -0.163165 | 0.268174 | 1.312135 | 1.241686 | 1.484484 | 1.279782 | -0.350179 | -0.266719 | -0.170434 | 0.147000 | -0.259175 | -0.323251 |
| 344 | 0.056854 | 0.089458 | -0.128149 | 0.123107 | -0.879175 | 0.172486 | 0.919301 | 0.727007 | -0.032509 | -0.594358 | -0.241536 | -0.339538 | -1.563800 |
| 345 | 1.322735 | -0.970372 | -1.058427 | 1.018282 | -1.228871 | 0.835533 | 1.462831 | -1.481872 | -2.024441 | 0.388890 | 2.395768 | -0.993539 | 0.301816 |
| 346 | -0.159679 | -0.200313 | -0.181878 | 0.221536 | -0.604018 | 0.554979 | 0.173592 | -1.137738 | -1.525377 | -0.382164 | 1.156959 | 0.545188 | -0.873936 |
| 347 | -0.510690 | -0.141874 | -0.170690 | -0.486309 | -1.066447 | -1.098392 | -1.513393 | -0.202811 | 0.062343 | 0.446348 | -0.029988 | -0.024432 | -0.978036 |
| 348 | 0.441393 | 0.403987 | 0.538948 | 1.253198 | -0.158511 | 0.497768 | 0.151471 | -0.006025 | 0.213458 | 0.119760 | -0.002312 | 0.139434 | -0.401118 |
| 349 | 0.548477 | 0.987769 | 0.505748 | 0.779668 | 0.504327 | -0.003400 | 0.200264 | 0.287803 | 0.084852 | -0.044437 | 0.769553 | 0.169816 | -0.581506 |
| 350 | 0.278851 | -0.150632 | 1.015313 | 0.158731 | -1.435466 | -0.910636 | 1.526971 | 0.810376 | -0.088268 | 2.273901 | 1.895682 | -0.573207 | 1.173543 |
| 351 | 1.781784 | -0.680962 | -0.140043 | 1.730156 | 0.760657 | 1.081874 | 0.686370 | -0.456141 | -0.310319 | 0.443108 | 0.067726 | -0.804283 | 0.268616 |
| 352 | 1.110023 | -0.419764 | -0.451242 | 1.471440 | 0.860531 | 0.858025 | 1.016472 | 0.013533 | -0.532955 | 0.597255 | -0.385255 | -1.299309 | 0.869963 |
| 353 | 0.463780 | 0.094111 | 0.074193 | 0.457058 | -0.494585 | -0.741218 | -1.615368 | -0.323890 | 0.179301 | -0.914854 | -0.881275 | -0.284568 | 0.516848 |
| 354 | 0.162857 | 1.300630 | -0.374191 | -0.148478 | -0.275205 | 0.936621 | -0.301931 | 0.926288 | -0.242039 | -1.217862 | -0.849053 | 0.381655 | 1.521222 |
| 355 | -0.261040 | 1.897992 | 0.324175 | 0.250461 | -0.326921 | 0.078347 | -0.794723 | 1.245895 | 0.561437 | 0.299601 | 0.612062 | 0.375109 | 0.668225 |
| 356 | -2.412627 | -0.912657 | 0.924859 | 1.091412 | -0.430459 | 0.991776 | 0.577087 | 0.366311 | 0.916132 | -0.010096 | -0.337066 | 0.723121 | 0.634413 |
| 357 | -1.610420 | -0.171488 | 1.308910 | 1.557149 | -0.783120 | 1.055891 | 0.070922 | 0.736289 | 0.651236 | -0.209692 | -0.293388 | 0.549580 | 0.947465 |
| 358 | -1.627642 | -0.225022 | 1.420291 | 1.585386 | -0.623077 | 1.204209 | 0.203574 | 0.815228 | 0.701131 | -0.111706 | -0.352897 | 0.552444 | 1.038487 |
| 359 | 1.658650 | 0.261694 | 0.694273 | -0.634006 | -0.742717 | -1.107684 | -0.040641 | 0.685375 | 0.704374 | 0.457634 | -0.012812 | -0.227444 | -0.311482 |
| 360 | -0.472450 | 1.290735 | 1.251486 | 0.902820 | 1.064267 | 0.319911 | 0.273062 | -0.004026 | -0.730129 | -0.487802 | -0.590033 | 0.917054 | 0.316796 |
| 361 | 0.145973 | 1.078298 | -0.110458 | 0.396705 | 0.465683 | 0.120005 | 0.324478 | 0.647014 | 0.406366 | 0.303529 | 0.342183 | 0.418467 | -0.257006 |
| 362 | 1.354053 | 0.408020 | -1.449365 | -0.144038 | 0.735070 | 1.458916 | -0.253049 | 0.476118 | 1.309448 | 1.981607 | 0.319930 | -0.734588 | -2.427842 |
| 363 | 3.546326 | -0.337767 | -0.983896 | -3.155084 | 1.922015 | 3.128359 | 1.576092 | 2.767242 | 2.734920 | 1.749030 | -1.432287 | -5.486282 | -3.776088 |
| 364 | 3.564797 | -0.492960 | -0.663172 | -2.465245 | 2.044991 | 3.045697 | 1.746383 | 2.238430 | 2.806354 | 2.318786 | -0.732814 | -5.203217 | -4.762769 |
| 365 | -0.480041 | 0.390140 | 0.283493 | 0.710367 | 0.436247 | 0.787936 | 0.149057 | 1.081200 | 1.130496 | 0.783116 | 1.174331 | 0.987069 | 1.027523 |
| 366 | 1.601344 | 1.120977 | 0.942690 | 0.218542 | 1.432015 | 1.975393 | 1.352637 | 0.851851 | 0.707687 | 1.420656 | -1.301018 | 0.996552 | 2.286308 |
| 367 | -1.388425 | 0.554214 | 2.322455 | 0.125526 | 0.168411 | 1.459935 | -0.011567 | 0.377516 | 1.801634 | 1.061665 | 1.432895 | 1.553952 | 0.335629 |
| 368 | -0.560849 | 0.191976 | -1.558597 | -1.925355 | 0.006144 | 1.582531 | 2.334107 | 1.472221 | 0.788027 | -0.066399 | -0.539592 | -0.089987 | 1.081026 |
| 369 | -0.015724 | 0.095384 | -0.050287 | 0.330265 | -0.857518 | -1.110181 | 0.457976 | 1.235890 | 0.937447 | 1.294180 | 1.196429 | 1.964913 | 0.788473 |
| 370 | -0.320091 | 0.789370 | -0.347116 | -0.257819 | -0.264866 | 0.119392 | 0.174557 | -0.102622 | -0.147261 | 0.927949 | 1.494185 | 0.617596 | -0.119013 |
| 371 | -1.397911 | 0.969347 | -0.218602 | 0.165675 | -1.187201 | -1.022111 | 0.090487 | -0.281353 | -1.520146 | -0.950575 | -1.733689 | -0.924046 | -0.731033 |
372 rows × 13 columns
In [60]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[60]:
[4836.0, 4367.616182778379, 3974.0733975762073, 3720.4477504020774, 3549.9823424876267, 3386.093163495573, 3254.3815559758523, 3136.90996141146, 3048.6934734702136, 2957.186911982338, 2855.333306370868, 2802.0220333671496, 2712.5965714921504, 2657.189981994876]
In [89]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[89]:
[<matplotlib.lines.Line2D at 0x244598a8630>]
K=3
In [81]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[81]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [82]:
kmeans_mfcc.labels_
Out[82]:
array([1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 1, 1, 2, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0])
In [83]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[83]:
array([1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 1, 1, 2, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0])
In [84]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [85]:
X
Out[85]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.303210 | 1.253016 | -0.394054 | 0.592598 | 0.106623 | -2.083256 | 0.858313 | 0.801936 | 2.380580 | -1.304258 | 0.556361 | 1.949530 | -1.046692 | 1 | 0 |
| 1 | 0.647559 | -1.553511 | -1.648243 | -1.010792 | -0.857927 | 0.335856 | 0.884468 | 0.250703 | 0.298648 | 0.683922 | 1.599907 | 0.349480 | -0.484103 | 1 | 0 |
| 2 | 2.266625 | -0.333664 | 0.685765 | -2.001143 | -0.820018 | -2.442242 | -1.583451 | -2.793213 | -2.158376 | -2.431307 | -0.855856 | -0.471626 | -1.478884 | 0 | 0 |
| 3 | 0.672266 | -0.397422 | 0.105106 | -1.822060 | -1.335294 | -1.384110 | -0.608579 | -1.639581 | -2.081721 | -1.171877 | -1.102493 | -1.264328 | -1.165166 | 0 | 0 |
| 4 | 0.747622 | 0.110331 | -0.079109 | -1.108698 | -0.391749 | -0.448919 | 0.133859 | -0.843237 | -1.924086 | -0.222835 | 0.221819 | 0.017631 | -0.725177 | 0 | 0 |
| 5 | 2.072441 | -1.565884 | -0.268750 | -1.648648 | -3.149219 | -3.406801 | -4.332461 | -1.709990 | -1.313696 | -1.503431 | -2.036749 | -1.928213 | -2.657430 | 0 | 0 |
| 6 | -0.163654 | 0.470736 | 0.440011 | 0.594090 | -1.227236 | 0.409218 | 0.608496 | -0.948833 | -2.051031 | 0.892327 | 0.371683 | -0.318984 | 0.022251 | 0 | 0 |
| 7 | 0.477732 | -0.217651 | -0.908178 | -1.257961 | -1.360625 | -0.551388 | -0.599896 | 0.099066 | 1.299780 | 2.443060 | 0.417236 | -0.919898 | -0.916391 | 1 | 0 |
| 8 | 0.897786 | -3.040013 | 0.311694 | -0.386220 | -0.321124 | -0.221380 | -0.207002 | -0.127210 | 0.011073 | 0.211925 | -0.400748 | -0.296623 | -0.143419 | 0 | 0 |
| 9 | -0.977087 | 1.088438 | -0.184899 | -0.626934 | 0.577247 | 0.522552 | 1.150101 | 1.023214 | 0.136257 | 0.193237 | -0.496760 | 0.367549 | 0.378679 | 1 | 0 |
| 10 | 0.328615 | -1.292300 | -1.398337 | -0.677268 | -1.070980 | -0.075073 | -0.740061 | -0.424240 | -0.216693 | 0.633892 | -0.070397 | 0.960392 | 0.403827 | 0 | 0 |
| 11 | -0.199470 | 0.110219 | 0.238637 | 0.455154 | -0.116209 | 0.374450 | 0.078145 | 0.424005 | 0.633052 | -0.153498 | -0.647002 | 0.301135 | -0.000406 | 1 | 0 |
| 12 | -0.711256 | 0.124802 | 0.734425 | -0.445078 | -0.503247 | -0.323539 | 0.236246 | -0.572803 | -0.221112 | -0.206486 | -0.180516 | 0.119335 | 0.027470 | 0 | 0 |
| 13 | -0.806898 | -0.126740 | -0.383726 | 0.035489 | -1.164460 | -0.574335 | -0.633858 | -0.009812 | -0.131411 | 0.549197 | -0.257952 | 0.307916 | 0.814674 | 0 | 0 |
| 14 | -0.077242 | -0.331495 | 0.550493 | -0.008575 | -0.215759 | -1.260552 | -0.581296 | 0.369790 | -0.684267 | 0.792489 | -0.457321 | -0.704205 | -0.093986 | 0 | 0 |
| 15 | 0.244538 | 0.777957 | 0.464181 | 0.169574 | -0.433604 | -1.172185 | -1.866928 | 0.759778 | -0.372608 | 0.009766 | 0.964104 | 1.082661 | -0.506505 | 0 | 0 |
| 16 | 0.602329 | -0.035069 | 0.178352 | -0.036690 | 0.180302 | -0.769568 | 0.364535 | 0.996915 | 0.263984 | -0.829872 | -0.133422 | 0.601135 | -1.217336 | 1 | 0 |
| 17 | -0.570258 | -0.759570 | 0.108993 | 0.657477 | 0.342355 | -0.903388 | 0.112467 | -0.669060 | -0.661619 | 0.915675 | 1.620722 | -0.160697 | 0.379275 | 0 | 0 |
| 18 | -0.288268 | -1.202534 | -0.544058 | 0.295908 | 0.568680 | -1.416228 | 0.423676 | 0.041836 | -0.665694 | 0.699155 | -0.070704 | -0.429451 | 1.194384 | 0 | 0 |
| 19 | -0.497305 | -0.552590 | 0.332470 | 0.660607 | 0.293725 | -0.945647 | -1.269354 | 0.464095 | 1.166255 | 2.034233 | 2.037855 | 0.555927 | 0.423683 | 1 | 0 |
| 20 | 1.386141 | -0.516432 | -0.074640 | 0.751101 | -1.151864 | 0.155819 | -1.921431 | -3.381158 | -1.145758 | -1.197084 | 0.654749 | 1.636425 | 0.993236 | 0 | 0 |
| 21 | 0.076772 | 0.072900 | 0.122544 | 0.799017 | -1.121011 | -0.137599 | -1.150187 | -1.669293 | -1.110882 | -0.047217 | -0.034112 | -0.659214 | 1.160642 | 0 | 0 |
| 22 | 0.670757 | -0.167252 | -0.352765 | 0.189499 | -1.232602 | -0.168579 | -1.559900 | -1.850665 | -1.416478 | 0.031846 | 0.308193 | -0.956133 | 0.507231 | 0 | 0 |
| 23 | 0.639283 | 0.699164 | 0.621380 | -0.725771 | -0.890352 | 0.643955 | -1.097228 | 0.229756 | -0.091793 | -2.390193 | -0.825768 | -2.164531 | -0.772983 | 1 | 0 |
| 24 | -0.907399 | 2.155157 | 0.873522 | 1.655111 | 0.871099 | 1.083262 | -0.186962 | 0.373227 | -0.354082 | 0.573586 | 0.733097 | -0.986481 | 0.727511 | 1 | 0 |
| 25 | -1.152272 | 1.601470 | 0.221927 | 1.296592 | 0.572807 | 0.581774 | -0.479257 | 0.209504 | -0.636178 | 0.574450 | 0.615706 | -0.877894 | 0.941827 | 1 | 0 |
| 26 | -0.676596 | 0.405600 | 0.553370 | 0.691531 | -0.292469 | 0.626694 | 0.080413 | 0.246868 | -0.100975 | 0.606694 | -0.024154 | -1.553730 | -0.210884 | 1 | 0 |
| 27 | -0.704834 | -0.058170 | 0.609171 | -0.735340 | -0.512747 | 0.796018 | -0.405976 | 0.502120 | 0.717380 | -1.625431 | 0.825742 | -1.663942 | -0.379395 | 1 | 0 |
| 28 | 0.273095 | 1.014503 | -0.772750 | -0.065028 | -0.513485 | 0.235377 | -0.266144 | 1.373964 | 0.711880 | -1.261758 | 1.106463 | 0.515863 | 0.555866 | 1 | 0 |
| 29 | 1.216372 | 0.637021 | 0.649194 | 0.099873 | -0.816614 | 0.555439 | -1.272918 | -0.035862 | 0.154194 | -1.797465 | -0.177830 | -1.702118 | -1.136716 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 342 | 0.056741 | 0.169776 | 0.434163 | -0.208821 | 0.498957 | 0.349198 | 0.547068 | 0.250228 | -0.743894 | -0.497075 | 0.373497 | -0.025547 | 0.260645 | 1 | 1 |
| 343 | -0.845812 | -0.163165 | 0.268174 | 1.312135 | 1.241686 | 1.484484 | 1.279782 | -0.350179 | -0.266719 | -0.170434 | 0.147000 | -0.259175 | -0.323251 | 1 | 1 |
| 344 | 0.056854 | 0.089458 | -0.128149 | 0.123107 | -0.879175 | 0.172486 | 0.919301 | 0.727007 | -0.032509 | -0.594358 | -0.241536 | -0.339538 | -1.563800 | 1 | 1 |
| 345 | 1.322735 | -0.970372 | -1.058427 | 1.018282 | -1.228871 | 0.835533 | 1.462831 | -1.481872 | -2.024441 | 0.388890 | 2.395768 | -0.993539 | 0.301816 | 0 | 1 |
| 346 | -0.159679 | -0.200313 | -0.181878 | 0.221536 | -0.604018 | 0.554979 | 0.173592 | -1.137738 | -1.525377 | -0.382164 | 1.156959 | 0.545188 | -0.873936 | 0 | 1 |
| 347 | -0.510690 | -0.141874 | -0.170690 | -0.486309 | -1.066447 | -1.098392 | -1.513393 | -0.202811 | 0.062343 | 0.446348 | -0.029988 | -0.024432 | -0.978036 | 0 | 1 |
| 348 | 0.441393 | 0.403987 | 0.538948 | 1.253198 | -0.158511 | 0.497768 | 0.151471 | -0.006025 | 0.213458 | 0.119760 | -0.002312 | 0.139434 | -0.401118 | 1 | 1 |
| 349 | 0.548477 | 0.987769 | 0.505748 | 0.779668 | 0.504327 | -0.003400 | 0.200264 | 0.287803 | 0.084852 | -0.044437 | 0.769553 | 0.169816 | -0.581506 | 1 | 1 |
| 350 | 0.278851 | -0.150632 | 1.015313 | 0.158731 | -1.435466 | -0.910636 | 1.526971 | 0.810376 | -0.088268 | 2.273901 | 1.895682 | -0.573207 | 1.173543 | 1 | 1 |
| 351 | 1.781784 | -0.680962 | -0.140043 | 1.730156 | 0.760657 | 1.081874 | 0.686370 | -0.456141 | -0.310319 | 0.443108 | 0.067726 | -0.804283 | 0.268616 | 1 | 1 |
| 352 | 1.110023 | -0.419764 | -0.451242 | 1.471440 | 0.860531 | 0.858025 | 1.016472 | 0.013533 | -0.532955 | 0.597255 | -0.385255 | -1.299309 | 0.869963 | 1 | 1 |
| 353 | 0.463780 | 0.094111 | 0.074193 | 0.457058 | -0.494585 | -0.741218 | -1.615368 | -0.323890 | 0.179301 | -0.914854 | -0.881275 | -0.284568 | 0.516848 | 0 | 1 |
| 354 | 0.162857 | 1.300630 | -0.374191 | -0.148478 | -0.275205 | 0.936621 | -0.301931 | 0.926288 | -0.242039 | -1.217862 | -0.849053 | 0.381655 | 1.521222 | 1 | 1 |
| 355 | -0.261040 | 1.897992 | 0.324175 | 0.250461 | -0.326921 | 0.078347 | -0.794723 | 1.245895 | 0.561437 | 0.299601 | 0.612062 | 0.375109 | 0.668225 | 1 | 1 |
| 356 | -2.412627 | -0.912657 | 0.924859 | 1.091412 | -0.430459 | 0.991776 | 0.577087 | 0.366311 | 0.916132 | -0.010096 | -0.337066 | 0.723121 | 0.634413 | 1 | 1 |
| 357 | -1.610420 | -0.171488 | 1.308910 | 1.557149 | -0.783120 | 1.055891 | 0.070922 | 0.736289 | 0.651236 | -0.209692 | -0.293388 | 0.549580 | 0.947465 | 1 | 1 |
| 358 | -1.627642 | -0.225022 | 1.420291 | 1.585386 | -0.623077 | 1.204209 | 0.203574 | 0.815228 | 0.701131 | -0.111706 | -0.352897 | 0.552444 | 1.038487 | 1 | 1 |
| 359 | 1.658650 | 0.261694 | 0.694273 | -0.634006 | -0.742717 | -1.107684 | -0.040641 | 0.685375 | 0.704374 | 0.457634 | -0.012812 | -0.227444 | -0.311482 | 1 | 1 |
| 360 | -0.472450 | 1.290735 | 1.251486 | 0.902820 | 1.064267 | 0.319911 | 0.273062 | -0.004026 | -0.730129 | -0.487802 | -0.590033 | 0.917054 | 0.316796 | 1 | 1 |
| 361 | 0.145973 | 1.078298 | -0.110458 | 0.396705 | 0.465683 | 0.120005 | 0.324478 | 0.647014 | 0.406366 | 0.303529 | 0.342183 | 0.418467 | -0.257006 | 1 | 1 |
| 362 | 1.354053 | 0.408020 | -1.449365 | -0.144038 | 0.735070 | 1.458916 | -0.253049 | 0.476118 | 1.309448 | 1.981607 | 0.319930 | -0.734588 | -2.427842 | 1 | 1 |
| 363 | 3.546326 | -0.337767 | -0.983896 | -3.155084 | 1.922015 | 3.128359 | 1.576092 | 2.767242 | 2.734920 | 1.749030 | -1.432287 | -5.486282 | -3.776088 | 2 | 1 |
| 364 | 3.564797 | -0.492960 | -0.663172 | -2.465245 | 2.044991 | 3.045697 | 1.746383 | 2.238430 | 2.806354 | 2.318786 | -0.732814 | -5.203217 | -4.762769 | 2 | 1 |
| 365 | -0.480041 | 0.390140 | 0.283493 | 0.710367 | 0.436247 | 0.787936 | 0.149057 | 1.081200 | 1.130496 | 0.783116 | 1.174331 | 0.987069 | 1.027523 | 1 | 1 |
| 366 | 1.601344 | 1.120977 | 0.942690 | 0.218542 | 1.432015 | 1.975393 | 1.352637 | 0.851851 | 0.707687 | 1.420656 | -1.301018 | 0.996552 | 2.286308 | 1 | 1 |
| 367 | -1.388425 | 0.554214 | 2.322455 | 0.125526 | 0.168411 | 1.459935 | -0.011567 | 0.377516 | 1.801634 | 1.061665 | 1.432895 | 1.553952 | 0.335629 | 1 | 1 |
| 368 | -0.560849 | 0.191976 | -1.558597 | -1.925355 | 0.006144 | 1.582531 | 2.334107 | 1.472221 | 0.788027 | -0.066399 | -0.539592 | -0.089987 | 1.081026 | 1 | 1 |
| 369 | -0.015724 | 0.095384 | -0.050287 | 0.330265 | -0.857518 | -1.110181 | 0.457976 | 1.235890 | 0.937447 | 1.294180 | 1.196429 | 1.964913 | 0.788473 | 1 | 1 |
| 370 | -0.320091 | 0.789370 | -0.347116 | -0.257819 | -0.264866 | 0.119392 | 0.174557 | -0.102622 | -0.147261 | 0.927949 | 1.494185 | 0.617596 | -0.119013 | 1 | 1 |
| 371 | -1.397911 | 0.969347 | -0.218602 | 0.165675 | -1.187201 | -1.022111 | 0.090487 | -0.281353 | -1.520146 | -0.950575 | -1.733689 | -0.924046 | -0.731033 | 0 | 1 |
372 rows × 15 columns
In [86]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[86]:
<matplotlib.axes._subplots.AxesSubplot at 0x2445d307358>
In [16]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [193]:
X = df_n_ps_std_mfcc[1]
In [194]:
y = df_n_ps[1]['chosen']
In [195]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [196]:
X_train.shape
Out[196]:
(191, 13)
In [47]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [48]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [49]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [50]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20, 20, 20), 'learning_rate_init': 0.01, 'max_iter': 2000}, que permiten obtener un Accuracy de 81.68% y un Kappa del 52.97
Tiempo total: 25.07 minutos
In [197]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20,20,20]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.01
epochs = 2000
In [198]:
input_tensor = Input(shape = (n0,))
In [199]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [200]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [201]:
model.summary()
Model: "model_7" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_7 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_19 (Dense) (None, 20) 280 _________________________________________________________________ dense_20 (Dense) (None, 20) 420 _________________________________________________________________ dense_21 (Dense) (None, 20) 420 _________________________________________________________________ dense_22 (Dense) (None, 1) 21 ================================================================= Total params: 1,141 Trainable params: 1,141 Non-trainable params: 0 _________________________________________________________________
In [202]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size= 32,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/2000 191/191 [==============================] - 0s 1ms/step - loss: 0.6261 - accuracy: 0.6387 - val_loss: 0.5919 - val_accuracy: 0.7031 Epoch 2/2000 191/191 [==============================] - 0s 63us/step - loss: 0.4675 - accuracy: 0.7487 - val_loss: 0.5618 - val_accuracy: 0.7188 Epoch 3/2000 191/191 [==============================] - 0s 68us/step - loss: 0.4415 - accuracy: 0.7906 - val_loss: 0.6494 - val_accuracy: 0.7500 Epoch 4/2000 191/191 [==============================] - 0s 68us/step - loss: 0.3992 - accuracy: 0.8220 - val_loss: 0.6424 - val_accuracy: 0.7188 Epoch 5/2000 191/191 [==============================] - 0s 68us/step - loss: 0.3713 - accuracy: 0.8429 - val_loss: 0.6325 - val_accuracy: 0.7344 Epoch 6/2000 191/191 [==============================] - 0s 63us/step - loss: 0.3457 - accuracy: 0.8586 - val_loss: 0.6120 - val_accuracy: 0.7188 Epoch 7/2000 191/191 [==============================] - 0s 58us/step - loss: 0.3136 - accuracy: 0.8901 - val_loss: 0.6513 - val_accuracy: 0.7344 Epoch 8/2000 191/191 [==============================] - 0s 58us/step - loss: 0.2935 - accuracy: 0.9005 - val_loss: 0.6515 - val_accuracy: 0.7344 Epoch 9/2000 191/191 [==============================] - 0s 63us/step - loss: 0.2669 - accuracy: 0.8953 - val_loss: 0.6463 - val_accuracy: 0.7188 Epoch 10/2000 191/191 [==============================] - 0s 58us/step - loss: 0.2335 - accuracy: 0.9162 - val_loss: 0.6499 - val_accuracy: 0.7188 Epoch 11/2000 191/191 [==============================] - 0s 78us/step - loss: 0.2106 - accuracy: 0.9267 - val_loss: 0.7244 - val_accuracy: 0.7344 Epoch 12/2000 191/191 [==============================] - 0s 63us/step - loss: 0.1772 - accuracy: 0.9372 - val_loss: 0.7337 - val_accuracy: 0.7344 Epoch 13/2000 191/191 [==============================] - 0s 63us/step - loss: 0.1720 - accuracy: 0.9319 - val_loss: 0.8111 - val_accuracy: 0.7031 Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.004999999888241291. Epoch 14/2000 191/191 [==============================] - 0s 58us/step - loss: 0.1282 - accuracy: 0.9686 - val_loss: 0.7748 - val_accuracy: 0.7500 Epoch 15/2000 191/191 [==============================] - 0s 68us/step - loss: 0.1112 - accuracy: 0.9791 - val_loss: 0.8126 - val_accuracy: 0.7500 Epoch 16/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0983 - accuracy: 0.9791 - val_loss: 0.8576 - val_accuracy: 0.7500 Epoch 17/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0847 - accuracy: 0.9895 - val_loss: 0.8517 - val_accuracy: 0.7344 Epoch 18/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0736 - accuracy: 0.9948 - val_loss: 0.8483 - val_accuracy: 0.7656 Epoch 19/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0656 - accuracy: 0.9948 - val_loss: 0.9130 - val_accuracy: 0.7188 Epoch 20/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0547 - accuracy: 1.0000 - val_loss: 0.9393 - val_accuracy: 0.7188 Epoch 21/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0486 - accuracy: 1.0000 - val_loss: 0.9333 - val_accuracy: 0.7500 Epoch 22/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 1.0137 - val_accuracy: 0.7188 Epoch 23/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0341 - accuracy: 1.0000 - val_loss: 1.0446 - val_accuracy: 0.7344 Epoch 24/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0278 - accuracy: 1.0000 - val_loss: 1.0305 - val_accuracy: 0.7500 Epoch 25/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0247 - accuracy: 1.0000 - val_loss: 1.0528 - val_accuracy: 0.7188 Epoch 26/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0211 - accuracy: 1.0000 - val_loss: 1.1001 - val_accuracy: 0.7500 Epoch 27/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 1.1188 - val_accuracy: 0.7500 Epoch 28/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 1.1365 - val_accuracy: 0.7344 Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455. Epoch 29/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 1.1481 - val_accuracy: 0.7344 Epoch 30/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 1.1627 - val_accuracy: 0.7500 Epoch 31/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 1.1728 - val_accuracy: 0.7500 Epoch 32/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 1.1814 - val_accuracy: 0.7500 Epoch 33/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 1.1954 - val_accuracy: 0.7500 Epoch 34/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 1.2078 - val_accuracy: 0.7344 Epoch 35/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 1.2148 - val_accuracy: 0.7500 Epoch 36/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.2184 - val_accuracy: 0.7344 Epoch 37/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0091 - accuracy: 1.0000 - val_loss: 1.2278 - val_accuracy: 0.7344 Epoch 38/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 1.2391 - val_accuracy: 0.7344 Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228. Epoch 39/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 1.2467 - val_accuracy: 0.7344 Epoch 40/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.2504 - val_accuracy: 0.7344 Epoch 41/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 1.2559 - val_accuracy: 0.7344 Epoch 42/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 1.2602 - val_accuracy: 0.7344 Epoch 43/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 1.2626 - val_accuracy: 0.7500 Epoch 44/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 1.2679 - val_accuracy: 0.7344 Epoch 45/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 1.2688 - val_accuracy: 0.7344 Epoch 46/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.2745 - val_accuracy: 0.7344 Epoch 47/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 1.2803 - val_accuracy: 0.7344 Epoch 48/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 1.2839 - val_accuracy: 0.7344 Epoch 00048: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614. Epoch 49/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 1.2861 - val_accuracy: 0.7344 Epoch 50/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 1.2886 - val_accuracy: 0.7344 Epoch 51/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 1.2901 - val_accuracy: 0.7344 Epoch 52/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 1.2916 - val_accuracy: 0.7344 Epoch 53/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 1.2941 - val_accuracy: 0.7344 Epoch 54/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 1.2960 - val_accuracy: 0.7344 Epoch 55/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 1.2973 - val_accuracy: 0.7344 Epoch 56/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.2994 - val_accuracy: 0.7344 Epoch 57/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.3012 - val_accuracy: 0.7344 Epoch 58/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.3040 - val_accuracy: 0.7344 Epoch 00058: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807. Epoch 59/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.3054 - val_accuracy: 0.7344 Epoch 60/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.3067 - val_accuracy: 0.7344 Epoch 61/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.3080 - val_accuracy: 0.7344 Epoch 62/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 1.3096 - val_accuracy: 0.7344 Epoch 63/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 1.3105 - val_accuracy: 0.7344 Epoch 64/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 1.3117 - val_accuracy: 0.7344 Epoch 65/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.3128 - val_accuracy: 0.7344 Epoch 66/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.3135 - val_accuracy: 0.7344 Epoch 67/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.3143 - val_accuracy: 0.7344 Epoch 68/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.3152 - val_accuracy: 0.7344 Epoch 00068: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035. Epoch 69/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.3161 - val_accuracy: 0.7344 Epoch 70/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.3164 - val_accuracy: 0.7344 Epoch 71/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.3170 - val_accuracy: 0.7344 Epoch 72/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.3178 - val_accuracy: 0.7344 Epoch 73/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3184 - val_accuracy: 0.7344 Epoch 74/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3189 - val_accuracy: 0.7344 Epoch 75/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3193 - val_accuracy: 0.7344 Epoch 76/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3203 - val_accuracy: 0.7344 Epoch 77/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3209 - val_accuracy: 0.7344 Epoch 78/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3217 - val_accuracy: 0.7344 Epoch 00078: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05. Epoch 79/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.3219 - val_accuracy: 0.7344 Epoch 80/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3223 - val_accuracy: 0.7344 Epoch 81/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3226 - val_accuracy: 0.7344 Epoch 82/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3229 - val_accuracy: 0.7344 Epoch 83/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3233 - val_accuracy: 0.7344 Epoch 84/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3235 - val_accuracy: 0.7344 Epoch 85/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3239 - val_accuracy: 0.7344 Epoch 86/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3242 - val_accuracy: 0.7344 Epoch 87/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3245 - val_accuracy: 0.7344 Epoch 88/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3248 - val_accuracy: 0.7344 Epoch 00088: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05. Epoch 89/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3250 - val_accuracy: 0.7344 Epoch 90/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3251 - val_accuracy: 0.7344 Epoch 91/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3252 - val_accuracy: 0.7344 Epoch 92/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3255 - val_accuracy: 0.7344 Epoch 93/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3256 - val_accuracy: 0.7344 Epoch 94/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3257 - val_accuracy: 0.7344 Epoch 95/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3258 - val_accuracy: 0.7344 Epoch 96/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3260 - val_accuracy: 0.7344 Epoch 97/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3262 - val_accuracy: 0.7344 Epoch 98/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3264 - val_accuracy: 0.7344 Epoch 00098: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05. Epoch 99/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3265 - val_accuracy: 0.7344 Epoch 100/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3266 - val_accuracy: 0.7344 Epoch 101/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3267 - val_accuracy: 0.7344 Epoch 102/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3267 - val_accuracy: 0.7344 Epoch 103/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3268 - val_accuracy: 0.7344 Epoch 104/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3269 - val_accuracy: 0.7344 Epoch 105/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3270 - val_accuracy: 0.7344 Epoch 106/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3271 - val_accuracy: 0.7344 Epoch 107/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3272 - val_accuracy: 0.7344 Epoch 108/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3273 - val_accuracy: 0.7344 Epoch 00108: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06. Epoch 109/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3273 - val_accuracy: 0.7344 Epoch 110/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3274 - val_accuracy: 0.7344 Epoch 111/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3274 - val_accuracy: 0.7344 Epoch 112/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3274 - val_accuracy: 0.7344 Epoch 113/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3275 - val_accuracy: 0.7344 Epoch 114/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3275 - val_accuracy: 0.7344 Epoch 115/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3276 - val_accuracy: 0.7344 Epoch 116/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3276 - val_accuracy: 0.7344 Epoch 117/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3277 - val_accuracy: 0.7344 Epoch 118/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3277 - val_accuracy: 0.7344 Epoch 00118: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06. Epoch 119/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3278 - val_accuracy: 0.7344 Epoch 120/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3278 - val_accuracy: 0.7344 Epoch 121/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3278 - val_accuracy: 0.7344 Epoch 122/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3278 - val_accuracy: 0.7344 Epoch 123/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3278 - val_accuracy: 0.7344 Epoch 124/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3279 - val_accuracy: 0.7344 Epoch 125/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3279 - val_accuracy: 0.7344 Epoch 126/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3279 - val_accuracy: 0.7344 Epoch 127/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3279 - val_accuracy: 0.7344 Epoch 128/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 00128: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06. Epoch 129/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 130/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 131/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 132/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 133/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 134/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3280 - val_accuracy: 0.7344 Epoch 135/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 136/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 137/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 138/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 00138: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06. Epoch 139/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 140/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 141/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 142/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 143/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 144/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 145/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 146/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3281 - val_accuracy: 0.7344 Epoch 147/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 148/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00148: ReduceLROnPlateau reducing learning rate to 6.103515488575795e-07. Epoch 149/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 150/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 151/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 152/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 153/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 154/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 155/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 156/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 157/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 158/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00158: ReduceLROnPlateau reducing learning rate to 3.0517577442878974e-07. Epoch 159/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 160/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 161/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 162/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 163/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 164/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 165/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 166/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 167/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 168/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00168: ReduceLROnPlateau reducing learning rate to 1.5258788721439487e-07. Epoch 169/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 170/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 171/2000 191/191 [==============================] - ETA: 0s - loss: 0.0035 - accuracy: 1.00 - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 172/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 173/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 174/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 175/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 176/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 177/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 178/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00178: ReduceLROnPlateau reducing learning rate to 7.629394360719743e-08. Epoch 179/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 180/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 181/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 182/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 183/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 184/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 185/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 186/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 187/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 188/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00188: ReduceLROnPlateau reducing learning rate to 3.814697180359872e-08. Epoch 189/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 190/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 191/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 192/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 193/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 194/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 195/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 196/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 197/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 198/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00198: ReduceLROnPlateau reducing learning rate to 1.907348590179936e-08. Epoch 199/2000 191/191 [==============================] - 0s 52us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 200/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 201/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 202/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 203/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 204/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 205/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 206/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 207/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 208/2000 191/191 [==============================] - 0s 79us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00208: ReduceLROnPlateau reducing learning rate to 9.53674295089968e-09. Epoch 209/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 210/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 211/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 212/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 213/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 214/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 215/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 216/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 217/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 218/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00218: ReduceLROnPlateau reducing learning rate to 4.76837147544984e-09. Epoch 219/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 220/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 221/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 222/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 223/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 224/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 225/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 226/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 227/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 228/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00228: ReduceLROnPlateau reducing learning rate to 2.38418573772492e-09. Epoch 229/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 230/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 231/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 232/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 233/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 234/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 235/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 236/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 237/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 238/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00238: ReduceLROnPlateau reducing learning rate to 1.19209286886246e-09. Epoch 239/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 240/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 241/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 242/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 243/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 244/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 245/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 246/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 247/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 248/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00248: ReduceLROnPlateau reducing learning rate to 5.9604643443123e-10. Epoch 249/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 250/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 251/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 252/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 253/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 254/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 255/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 256/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 257/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 258/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00258: ReduceLROnPlateau reducing learning rate to 2.98023217215615e-10. Epoch 259/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 260/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 261/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 262/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 263/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 264/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 265/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 266/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 267/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 268/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00268: ReduceLROnPlateau reducing learning rate to 1.490116086078075e-10. Epoch 269/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 270/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 271/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 272/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 273/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 274/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 275/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 276/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 277/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 278/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00278: ReduceLROnPlateau reducing learning rate to 7.450580430390374e-11. Epoch 279/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 280/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 281/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 282/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 283/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 284/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 285/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 286/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 287/2000 191/191 [==============================] - 0s 79us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 288/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00288: ReduceLROnPlateau reducing learning rate to 3.725290215195187e-11. Epoch 289/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 290/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 291/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 292/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 293/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 294/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 295/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 296/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 297/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 298/2000 191/191 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 1.00 - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00298: ReduceLROnPlateau reducing learning rate to 1.8626451075975936e-11. Epoch 299/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 300/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 301/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 302/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 303/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 304/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 305/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 306/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 307/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 308/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00308: ReduceLROnPlateau reducing learning rate to 9.313225537987968e-12. Epoch 309/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 310/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 311/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 312/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 313/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 314/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 315/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 316/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 317/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 318/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00318: ReduceLROnPlateau reducing learning rate to 4.656612768993984e-12. Epoch 319/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 320/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 321/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 322/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 323/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 324/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 325/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 326/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 327/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 328/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00328: ReduceLROnPlateau reducing learning rate to 2.328306384496992e-12. Epoch 329/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 330/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 331/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 332/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 333/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 334/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 335/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 336/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 337/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 338/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00338: ReduceLROnPlateau reducing learning rate to 1.164153192248496e-12. Epoch 339/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 340/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 341/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 342/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 343/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 344/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 345/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 346/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 347/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 348/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00348: ReduceLROnPlateau reducing learning rate to 5.82076596124248e-13. Epoch 349/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 350/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 351/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 352/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 353/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 354/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 355/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 356/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 357/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 358/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00358: ReduceLROnPlateau reducing learning rate to 2.91038298062124e-13. Epoch 359/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 360/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 361/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 362/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 363/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 364/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 365/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 366/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 367/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 368/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00368: ReduceLROnPlateau reducing learning rate to 1.45519149031062e-13. Epoch 369/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 370/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 371/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 372/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 373/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 374/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 375/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 376/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 377/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 378/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00378: ReduceLROnPlateau reducing learning rate to 7.2759574515531e-14. Epoch 379/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 380/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 381/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 382/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 383/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 384/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 385/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 386/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 387/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 388/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00388: ReduceLROnPlateau reducing learning rate to 3.63797872577655e-14. Epoch 389/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 390/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 391/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 392/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 393/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 394/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 395/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 396/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 397/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 398/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00398: ReduceLROnPlateau reducing learning rate to 1.818989362888275e-14. Epoch 399/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 400/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 401/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 402/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 403/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 404/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 405/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 406/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 407/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 408/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00408: ReduceLROnPlateau reducing learning rate to 9.094946814441375e-15. Epoch 409/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 410/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 411/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 412/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 413/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 414/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 415/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 416/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 417/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 418/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00418: ReduceLROnPlateau reducing learning rate to 4.5474734072206875e-15. Epoch 419/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 420/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 421/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 422/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 423/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 424/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 425/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 426/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 427/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 428/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00428: ReduceLROnPlateau reducing learning rate to 2.2737367036103438e-15. Epoch 429/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 430/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 431/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 432/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 433/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 434/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 435/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 436/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 437/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 438/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00438: ReduceLROnPlateau reducing learning rate to 1.1368683518051719e-15. Epoch 439/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 440/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 441/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 442/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 443/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 444/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 445/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 446/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 447/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 448/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00448: ReduceLROnPlateau reducing learning rate to 5.684341759025859e-16. Epoch 449/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 450/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 451/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 452/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 453/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 454/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 455/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 456/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 457/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 458/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00458: ReduceLROnPlateau reducing learning rate to 2.8421708795129297e-16. Epoch 459/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 460/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 461/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 462/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 463/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 464/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 465/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 466/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 467/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 468/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00468: ReduceLROnPlateau reducing learning rate to 1.4210854397564648e-16. Epoch 469/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 470/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 471/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 472/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 473/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 474/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 475/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 476/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 477/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 478/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00478: ReduceLROnPlateau reducing learning rate to 7.105427198782324e-17. Epoch 479/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 480/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 481/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 482/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 483/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 484/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 485/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 486/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 487/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 488/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00488: ReduceLROnPlateau reducing learning rate to 3.552713599391162e-17. Epoch 489/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 490/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 491/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 492/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 493/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 494/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 495/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 496/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 497/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 498/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00498: ReduceLROnPlateau reducing learning rate to 1.776356799695581e-17. Epoch 499/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 500/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 501/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 502/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 503/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 504/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 505/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 506/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 507/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 508/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00508: ReduceLROnPlateau reducing learning rate to 8.881783998477905e-18. Epoch 509/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 510/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 511/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 512/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 513/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 514/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 515/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 516/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 517/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 518/2000 191/191 [==============================] - 0s 52us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00518: ReduceLROnPlateau reducing learning rate to 4.440891999238953e-18. Epoch 519/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 520/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 521/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 522/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 523/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 524/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 525/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 526/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 527/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 528/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00528: ReduceLROnPlateau reducing learning rate to 2.2204459996194763e-18. Epoch 529/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 530/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 531/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 532/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 533/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 534/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 535/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 536/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 537/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 538/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00538: ReduceLROnPlateau reducing learning rate to 1.1102229998097382e-18. Epoch 539/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 540/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 541/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 542/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 543/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 544/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 545/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 546/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 547/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 548/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00548: ReduceLROnPlateau reducing learning rate to 5.551114999048691e-19. Epoch 549/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 550/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 551/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 552/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 553/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 554/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 555/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 556/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 557/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 558/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00558: ReduceLROnPlateau reducing learning rate to 2.7755574995243454e-19. Epoch 559/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 560/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 561/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 562/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 563/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 564/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 565/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 566/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 567/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 568/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00568: ReduceLROnPlateau reducing learning rate to 1.3877787497621727e-19. Epoch 569/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 570/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 571/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 572/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 573/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 574/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 575/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 576/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 577/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 578/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00578: ReduceLROnPlateau reducing learning rate to 6.938893748810864e-20. Epoch 579/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 580/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 581/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 582/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 583/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 584/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 585/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 586/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 587/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 588/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00588: ReduceLROnPlateau reducing learning rate to 3.469446874405432e-20. Epoch 589/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 590/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 591/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 592/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 593/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 594/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 595/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 596/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 597/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 598/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00598: ReduceLROnPlateau reducing learning rate to 1.734723437202716e-20. Epoch 599/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 600/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 601/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 602/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 603/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 604/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 605/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 606/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 607/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 608/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00608: ReduceLROnPlateau reducing learning rate to 8.67361718601358e-21. Epoch 609/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 610/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 611/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 612/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 613/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 614/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 615/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 616/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 617/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 618/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00618: ReduceLROnPlateau reducing learning rate to 4.33680859300679e-21. Epoch 619/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 620/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 621/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 622/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 623/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 624/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 625/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 626/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 627/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 628/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00628: ReduceLROnPlateau reducing learning rate to 2.168404296503395e-21. Epoch 629/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 630/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 631/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 632/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 633/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 634/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 635/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 636/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 637/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 638/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00638: ReduceLROnPlateau reducing learning rate to 1.0842021482516974e-21. Epoch 639/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 640/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 641/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 642/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 643/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 644/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 645/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 646/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 647/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 648/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00648: ReduceLROnPlateau reducing learning rate to 5.421010741258487e-22. Epoch 649/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 650/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 651/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 652/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 653/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 654/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 655/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 656/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 657/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 658/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00658: ReduceLROnPlateau reducing learning rate to 2.7105053706292436e-22. Epoch 659/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 660/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 661/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 662/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 663/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 664/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 665/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 666/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 667/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 668/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00668: ReduceLROnPlateau reducing learning rate to 1.3552526853146218e-22. Epoch 669/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 670/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 671/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 672/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 673/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 674/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 675/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 676/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 677/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 678/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00678: ReduceLROnPlateau reducing learning rate to 6.776263426573109e-23. Epoch 679/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 680/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 681/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 682/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 683/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 684/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 685/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 686/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 687/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 688/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00688: ReduceLROnPlateau reducing learning rate to 3.3881317132865545e-23. Epoch 689/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 690/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 691/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 692/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 693/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 694/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 695/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 696/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 697/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 698/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00698: ReduceLROnPlateau reducing learning rate to 1.6940658566432772e-23. Epoch 699/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 700/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 701/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 702/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 703/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 704/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 705/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 706/2000 191/191 [==============================] - 0s 209us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 707/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 708/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00708: ReduceLROnPlateau reducing learning rate to 8.470329283216386e-24. Epoch 709/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 710/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 711/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 712/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 713/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 714/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 715/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 716/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 717/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 718/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00718: ReduceLROnPlateau reducing learning rate to 4.235164641608193e-24. Epoch 719/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 720/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 721/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 722/2000 191/191 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 1.00 - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 723/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 724/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 725/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 726/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 727/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 728/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00728: ReduceLROnPlateau reducing learning rate to 2.1175823208040965e-24. Epoch 729/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 730/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 731/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 732/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 733/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 734/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 735/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 736/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 737/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 738/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00738: ReduceLROnPlateau reducing learning rate to 1.0587911604020483e-24. Epoch 739/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 740/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 741/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 742/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 743/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 744/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 745/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 746/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 747/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 748/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00748: ReduceLROnPlateau reducing learning rate to 5.293955802010241e-25. Epoch 749/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 750/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 751/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 752/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 753/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 754/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 755/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 756/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 757/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 758/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00758: ReduceLROnPlateau reducing learning rate to 2.6469779010051207e-25. Epoch 759/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 760/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 761/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 762/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 763/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 764/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 765/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 766/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 767/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 768/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00768: ReduceLROnPlateau reducing learning rate to 1.3234889505025603e-25. Epoch 769/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 770/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 771/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 772/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 773/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 774/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 775/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 776/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 777/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 778/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00778: ReduceLROnPlateau reducing learning rate to 6.617444752512802e-26. Epoch 779/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 780/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 781/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 782/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 783/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 784/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 785/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 786/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 787/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 788/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00788: ReduceLROnPlateau reducing learning rate to 3.308722376256401e-26. Epoch 789/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 790/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 791/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 792/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 793/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 794/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 795/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 796/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 797/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 798/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00798: ReduceLROnPlateau reducing learning rate to 1.6543611881282004e-26. Epoch 799/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 800/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 801/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 802/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 803/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 804/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 805/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 806/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 807/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 808/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00808: ReduceLROnPlateau reducing learning rate to 8.271805940641002e-27. Epoch 809/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 810/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 811/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 812/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 813/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 814/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 815/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 816/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 817/2000 191/191 [==============================] - 0s 68us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 818/2000 191/191 [==============================] - 0s 58us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00818: ReduceLROnPlateau reducing learning rate to 4.135902970320501e-27. Epoch 819/2000 191/191 [==============================] - 0s 63us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 820/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 821/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 822/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 823/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 824/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 825/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 826/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 827/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 828/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00828: ReduceLROnPlateau reducing learning rate to 2.0679514851602505e-27. Epoch 829/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 830/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 831/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 832/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 833/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 834/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 835/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 836/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 837/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 838/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00838: ReduceLROnPlateau reducing learning rate to 1.0339757425801253e-27. Epoch 839/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 840/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 841/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 842/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 843/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 844/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 845/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 846/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 847/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 848/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00848: ReduceLROnPlateau reducing learning rate to 5.169878712900626e-28. Epoch 849/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 850/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 851/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 852/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 853/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 854/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 855/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 856/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 857/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 858/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00858: ReduceLROnPlateau reducing learning rate to 2.584939356450313e-28. Epoch 859/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 860/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 861/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 862/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 863/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 864/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 865/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 866/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 867/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 868/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00868: ReduceLROnPlateau reducing learning rate to 1.2924696782251566e-28. Epoch 869/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 870/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 871/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 872/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 873/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 874/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 875/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 876/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 877/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 878/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00878: ReduceLROnPlateau reducing learning rate to 6.462348391125783e-29. Epoch 879/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 880/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 881/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 882/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 883/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 884/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 885/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 886/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 887/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 888/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00888: ReduceLROnPlateau reducing learning rate to 3.2311741955628914e-29. Epoch 889/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 890/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 891/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 892/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 893/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 894/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 895/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 896/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 897/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 898/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00898: ReduceLROnPlateau reducing learning rate to 1.6155870977814457e-29. Epoch 899/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 900/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 901/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 902/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 903/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 904/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 905/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 906/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 907/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 908/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00908: ReduceLROnPlateau reducing learning rate to 8.077935488907229e-30. Epoch 909/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 910/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 911/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 912/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 913/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 914/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 915/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 916/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 917/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 918/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00918: ReduceLROnPlateau reducing learning rate to 4.038967744453614e-30. Epoch 919/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 920/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 921/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 922/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 923/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 924/2000 191/191 [==============================] - ETA: 0s - loss: 0.0058 - accuracy: 1.00 - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 925/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 926/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 927/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 928/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00928: ReduceLROnPlateau reducing learning rate to 2.019483872226807e-30. Epoch 929/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 930/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 931/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 932/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 933/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 934/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 935/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 936/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 937/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 938/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00938: ReduceLROnPlateau reducing learning rate to 1.0097419361134036e-30. Epoch 939/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 940/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 941/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 942/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 943/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 944/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 945/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 946/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 947/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 948/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00948: ReduceLROnPlateau reducing learning rate to 5.048709680567018e-31. Epoch 949/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 950/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 951/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 952/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 953/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 954/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 955/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 956/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 957/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 958/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00958: ReduceLROnPlateau reducing learning rate to 2.524354840283509e-31. Epoch 959/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 960/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 961/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 962/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 963/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 964/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 965/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 966/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 967/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 968/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00968: ReduceLROnPlateau reducing learning rate to 1.2621774201417545e-31. Epoch 969/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 970/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 971/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 972/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 973/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 974/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 975/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 976/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 977/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 978/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00978: ReduceLROnPlateau reducing learning rate to 6.310887100708772e-32. Epoch 979/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 980/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 981/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 982/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 983/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 984/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 985/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 986/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 987/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 988/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00988: ReduceLROnPlateau reducing learning rate to 3.155443550354386e-32. Epoch 989/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 990/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 991/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 992/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 993/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 994/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 995/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 996/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 997/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 998/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 00998: ReduceLROnPlateau reducing learning rate to 1.577721775177193e-32. Epoch 999/2000 191/191 [==============================] - 0s 157us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1000/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1001/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1002/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1003/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1004/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1005/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1006/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1007/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1008/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01008: ReduceLROnPlateau reducing learning rate to 7.888608875885965e-33. Epoch 1009/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1010/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1011/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1012/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1013/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1014/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1015/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1016/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1017/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1018/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01018: ReduceLROnPlateau reducing learning rate to 3.944304437942983e-33. Epoch 1019/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1020/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1021/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1022/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1023/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1024/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1025/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1026/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1027/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1028/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01028: ReduceLROnPlateau reducing learning rate to 1.9721522189714914e-33. Epoch 1029/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1030/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1031/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1032/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1033/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1034/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1035/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1036/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1037/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1038/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01038: ReduceLROnPlateau reducing learning rate to 9.860761094857457e-34. Epoch 1039/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1040/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1041/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1042/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1043/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1044/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1045/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1046/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1047/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1048/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01048: ReduceLROnPlateau reducing learning rate to 4.930380547428728e-34. Epoch 1049/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1050/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1051/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1052/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1053/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1054/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1055/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1056/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1057/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1058/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01058: ReduceLROnPlateau reducing learning rate to 2.465190273714364e-34. Epoch 1059/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1060/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1061/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1062/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1063/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1064/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1065/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1066/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1067/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1068/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01068: ReduceLROnPlateau reducing learning rate to 1.232595136857182e-34. Epoch 1069/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1070/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1071/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1072/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1073/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1074/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1075/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1076/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1077/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1078/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01078: ReduceLROnPlateau reducing learning rate to 6.16297568428591e-35. Epoch 1079/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1080/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1081/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1082/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1083/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1084/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1085/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1086/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1087/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1088/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01088: ReduceLROnPlateau reducing learning rate to 3.081487842142955e-35. Epoch 1089/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1090/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1091/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1092/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1093/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1094/2000 191/191 [==============================] - 0s 79us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1095/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1096/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1097/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1098/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01098: ReduceLROnPlateau reducing learning rate to 1.5407439210714776e-35. Epoch 1099/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1100/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1101/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1102/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1103/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1104/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1105/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1106/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1107/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1108/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01108: ReduceLROnPlateau reducing learning rate to 7.703719605357388e-36. Epoch 1109/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1110/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1111/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1112/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1113/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1114/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1115/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1116/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1117/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1118/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01118: ReduceLROnPlateau reducing learning rate to 3.851859802678694e-36. Epoch 1119/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1120/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1121/2000 191/191 [==============================] - 0s 136us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1122/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1123/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1124/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1125/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1126/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1127/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1128/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01128: ReduceLROnPlateau reducing learning rate to 1.925929901339347e-36. Epoch 1129/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1130/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1131/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1132/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1133/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1134/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1135/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1136/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1137/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1138/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01138: ReduceLROnPlateau reducing learning rate to 9.629649506696735e-37. Epoch 1139/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1140/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1141/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1142/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1143/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1144/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1145/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1146/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1147/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1148/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01148: ReduceLROnPlateau reducing learning rate to 4.8148247533483676e-37. Epoch 1149/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1150/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1151/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1152/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1153/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1154/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1155/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1156/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1157/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1158/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01158: ReduceLROnPlateau reducing learning rate to 2.4074123766741838e-37. Epoch 1159/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1160/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1161/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1162/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1163/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1164/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1165/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1166/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1167/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1168/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01168: ReduceLROnPlateau reducing learning rate to 1.2037061883370919e-37. Epoch 1169/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1170/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1171/2000 191/191 [==============================] - 0s 147us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1172/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1173/2000 191/191 [==============================] - 0s 162us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1174/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1175/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1176/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1177/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1178/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01178: ReduceLROnPlateau reducing learning rate to 6.018530941685459e-38. Epoch 1179/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1180/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1181/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1182/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1183/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1184/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1185/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1186/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1187/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1188/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01188: ReduceLROnPlateau reducing learning rate to 3.0092654708427297e-38. Epoch 1189/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1190/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1191/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1192/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1193/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1194/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1195/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1196/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1197/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1198/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01198: ReduceLROnPlateau reducing learning rate to 1.5046327354213649e-38. Epoch 1199/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1200/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1201/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1202/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1203/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1204/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1205/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1206/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1207/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1208/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01208: ReduceLROnPlateau reducing learning rate to 7.523163677106824e-39. Epoch 1209/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1210/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1211/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1212/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1213/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1214/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1215/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1216/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1217/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1218/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01218: ReduceLROnPlateau reducing learning rate to 3.761581838553412e-39. Epoch 1219/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1220/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1221/2000 191/191 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 1.00 - 0s 136us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1222/2000 191/191 [==============================] - 0s 136us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1223/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1224/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1225/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1226/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1227/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1228/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01228: ReduceLROnPlateau reducing learning rate to 1.88079056895209e-39. Epoch 1229/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1230/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1231/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1232/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1233/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1234/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1235/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1236/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1237/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1238/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01238: ReduceLROnPlateau reducing learning rate to 9.40395284476045e-40. Epoch 1239/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1240/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1241/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1242/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1243/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1244/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1245/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1246/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1247/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1248/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01248: ReduceLROnPlateau reducing learning rate to 4.701972919134064e-40. Epoch 1249/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1250/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1251/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1252/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1253/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1254/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1255/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1256/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1257/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1258/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01258: ReduceLROnPlateau reducing learning rate to 2.350986459567032e-40. Epoch 1259/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1260/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1261/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1262/2000 191/191 [==============================] - ETA: 0s - loss: 0.0072 - accuracy: 1.00 - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1263/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1264/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1265/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1266/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1267/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1268/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01268: ReduceLROnPlateau reducing learning rate to 1.175493229783516e-40. Epoch 1269/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1270/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1271/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1272/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1273/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1274/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1275/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1276/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1277/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1278/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01278: ReduceLROnPlateau reducing learning rate to 5.87746614891758e-41. Epoch 1279/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1280/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1281/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1282/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1283/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1284/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1285/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1286/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1287/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1288/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01288: ReduceLROnPlateau reducing learning rate to 2.93873307445879e-41. Epoch 1289/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1290/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1291/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1292/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1293/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1294/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1295/2000 191/191 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 1.00 - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1296/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1297/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1298/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01298: ReduceLROnPlateau reducing learning rate to 1.4694015696910032e-41. Epoch 1299/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1300/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1301/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1302/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1303/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1304/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1305/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1306/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1307/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1308/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01308: ReduceLROnPlateau reducing learning rate to 7.347007848455016e-42. Epoch 1309/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1310/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1311/2000 191/191 [==============================] - 0s 136us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1312/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1313/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1314/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1315/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1316/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1317/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1318/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01318: ReduceLROnPlateau reducing learning rate to 3.673503924227508e-42. Epoch 1319/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1320/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1321/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1322/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1323/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1324/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1325/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1326/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1327/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1328/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01328: ReduceLROnPlateau reducing learning rate to 1.8371022867298352e-42. Epoch 1329/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1330/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1331/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1332/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1333/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1334/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1335/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1336/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1337/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1338/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01338: ReduceLROnPlateau reducing learning rate to 9.185511433649176e-43. Epoch 1339/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1340/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1341/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1342/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1343/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1344/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1345/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1346/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1347/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1348/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01348: ReduceLROnPlateau reducing learning rate to 4.5962589629854e-43. Epoch 1349/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1350/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1351/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1352/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1353/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1354/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1355/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1356/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1357/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1358/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01358: ReduceLROnPlateau reducing learning rate to 2.2981294814927e-43. Epoch 1359/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1360/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1361/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1362/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1363/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1364/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1365/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1366/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1367/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1368/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01368: ReduceLROnPlateau reducing learning rate to 1.14906474074635e-43. Epoch 1369/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1370/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1371/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1372/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1373/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1374/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1375/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1376/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1377/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1378/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01378: ReduceLROnPlateau reducing learning rate to 5.74532370373175e-44. Epoch 1379/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1380/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1381/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1382/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1383/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1384/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1385/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1386/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1387/2000 191/191 [==============================] - ETA: 0s - loss: 0.0044 - accuracy: 1.00 - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1388/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01388: ReduceLROnPlateau reducing learning rate to 2.872661851865875e-44. Epoch 1389/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1390/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1391/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1392/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1393/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1394/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1395/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1396/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1397/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1398/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01398: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-44. Epoch 1399/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1400/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1401/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1402/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1403/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1404/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1405/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1406/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1407/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1408/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01408: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-45. Epoch 1409/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1410/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1411/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1412/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1413/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1414/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1415/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1416/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1417/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1418/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01418: ReduceLROnPlateau reducing learning rate to 3.5032461608120427e-45. Epoch 1419/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1420/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1421/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1422/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1423/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1424/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1425/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1426/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1427/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1428/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01428: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-45. Epoch 1429/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1430/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1431/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1432/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1433/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1434/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1435/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1436/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1437/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1438/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 01438: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-46. Epoch 1439/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1440/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1441/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1442/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1443/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1444/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1445/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1446/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1447/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1448/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1449/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1450/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1451/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1452/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1453/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1454/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1455/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1456/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1457/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1458/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1459/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1460/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1461/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1462/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1463/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1464/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1465/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1466/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1467/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1468/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1469/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1470/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1471/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1472/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1473/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1474/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1475/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1476/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1477/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1478/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1479/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1480/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1481/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1482/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1483/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1484/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1485/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1486/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1487/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1488/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1489/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1490/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1491/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1492/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1493/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1494/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1495/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1496/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1497/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1498/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1499/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1500/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1501/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1502/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1503/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1504/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1505/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1506/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1507/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1508/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1509/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1510/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1511/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1512/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1513/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1514/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1515/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1516/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1517/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1518/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1519/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1520/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1521/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1522/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1523/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1524/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1525/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1526/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1527/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1528/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1529/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1530/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1531/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1532/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1533/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1534/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1535/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1536/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1537/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1538/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1539/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1540/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1541/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1542/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1543/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1544/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1545/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1546/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1547/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1548/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1549/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1550/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1551/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1552/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1553/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1554/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1555/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1556/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1557/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1558/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1559/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1560/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1561/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1562/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1563/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1564/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1565/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1566/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1567/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1568/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1569/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1570/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1571/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1572/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1573/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1574/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1575/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1576/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1577/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1578/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1579/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1580/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1581/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1582/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1583/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1584/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1585/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1586/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1587/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1588/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1589/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1590/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1591/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1592/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1593/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1594/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1595/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1596/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1597/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1598/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1599/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1600/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1601/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1602/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1603/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1604/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1605/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1606/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1607/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1608/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1609/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1610/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1611/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1612/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1613/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1614/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1615/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1616/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1617/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1618/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1619/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1620/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1621/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1622/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1623/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1624/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1625/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1626/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1627/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1628/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1629/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1630/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1631/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1632/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1633/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1634/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1635/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1636/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1637/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1638/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1639/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1640/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1641/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1642/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1643/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1644/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1645/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1646/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1647/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1648/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1649/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1650/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1651/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1652/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1653/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1654/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1655/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1656/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1657/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1658/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1659/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1660/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1661/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1662/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1663/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1664/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1665/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1666/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1667/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1668/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1669/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1670/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1671/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1672/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1673/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1674/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1675/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1676/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1677/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1678/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1679/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1680/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1681/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1682/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1683/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1684/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1685/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1686/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1687/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1688/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1689/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1690/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1691/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1692/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1693/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1694/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1695/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1696/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1697/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1698/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1699/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1700/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1701/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1702/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1703/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1704/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1705/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1706/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1707/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1708/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1709/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1710/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1711/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1712/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1713/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1714/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1715/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1716/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1717/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1718/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1719/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1720/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1721/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1722/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1723/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1724/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1725/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1726/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1727/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1728/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1729/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1730/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1731/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1732/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1733/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1734/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1735/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1736/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1737/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1738/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1739/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1740/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1741/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1742/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1743/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1744/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1745/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1746/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1747/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1748/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1749/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1750/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1751/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1752/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1753/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1754/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1755/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1756/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1757/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1758/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1759/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1760/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1761/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1762/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1763/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1764/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1765/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1766/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1767/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1768/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1769/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1770/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1771/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1772/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1773/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1774/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1775/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1776/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1777/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1778/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1779/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1780/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1781/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1782/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1783/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1784/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1785/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1786/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1787/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1788/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1789/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1790/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1791/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1792/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1793/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1794/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1795/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1796/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1797/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1798/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1799/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1800/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1801/2000 191/191 [==============================] - ETA: 0s - loss: 0.0076 - accuracy: 1.00 - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1802/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1803/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1804/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1805/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1806/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1807/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1808/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1809/2000 191/191 [==============================] - 0s 199us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1810/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1811/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1812/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1813/2000 191/191 [==============================] - 0s 79us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1814/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1815/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1816/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1817/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1818/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1819/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1820/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1821/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1822/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1823/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1824/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1825/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1826/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1827/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1828/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1829/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1830/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1831/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1832/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1833/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1834/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1835/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1836/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1837/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1838/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1839/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1840/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1841/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1842/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1843/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1844/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1845/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1846/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1847/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1848/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1849/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1850/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1851/2000 191/191 [==============================] - 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0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1866/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1867/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1868/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1869/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1870/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1871/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1872/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1873/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1874/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1875/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1876/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1877/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1878/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1879/2000 191/191 [==============================] - 0s 73us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1880/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1881/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1882/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1883/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1884/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1885/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1886/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1887/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1888/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1889/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1890/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1891/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1892/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1893/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1894/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1895/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1896/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1897/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1898/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1899/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1900/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1901/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1902/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1903/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1904/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1905/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1906/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1907/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1908/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1909/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1910/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1911/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1912/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1913/2000 191/191 [==============================] - ETA: 0s - loss: 0.0088 - accuracy: 1.00 - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1914/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1915/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1916/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1917/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1918/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1919/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1920/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1921/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1922/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1923/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1924/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1925/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1926/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1927/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1928/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1929/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1930/2000 191/191 [==============================] - 0s 89us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1931/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1932/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1933/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1934/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1935/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1936/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1937/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1938/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1939/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1940/2000 191/191 [==============================] - 0s 78us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1941/2000 191/191 [==============================] - 0s 84us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1942/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1943/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1944/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1945/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1946/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1947/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1948/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1949/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1950/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1951/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1952/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1953/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1954/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1955/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1956/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1957/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1958/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1959/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1960/2000 191/191 [==============================] - ETA: 0s - loss: 0.0078 - accuracy: 1.00 - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1961/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1962/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1963/2000 191/191 [==============================] - 0s 131us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1964/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1965/2000 191/191 [==============================] - 0s 173us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1966/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1967/2000 191/191 [==============================] - 0s 126us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1968/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1969/2000 191/191 [==============================] - 0s 120us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1970/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1971/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1972/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1973/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1974/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1975/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1976/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1977/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1978/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1979/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1980/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1981/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1982/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1983/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1984/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1985/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1986/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1987/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1988/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1989/2000 191/191 [==============================] - 0s 94us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1990/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1991/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1992/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1993/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1994/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1995/2000 191/191 [==============================] - 0s 99us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1996/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1997/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1998/2000 191/191 [==============================] - 0s 115us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 1999/2000 191/191 [==============================] - 0s 110us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344 Epoch 2000/2000 191/191 [==============================] - 0s 105us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.3282 - val_accuracy: 0.7344
In [203]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 2000)
In [204]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 47us/step test loss: 1.3282248377799988, test accuracy: 0.734375
In [205]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.8006362672322375
In [206]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.39420935412026725
KMeans¶
In [66]:
X
Out[66]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 |
255 rows × 13 columns
In [67]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[67]:
[3315.0, 2972.7888695817974, 2748.18187155972, 2544.9420084212106, 2413.687059384553, 2278.037996783226, 2213.3487507256823, 2123.4282707474663, 2067.8299633414163, 1977.777252698108, 1956.5229777214513, 1880.0296166971755, 1815.5096049846275, 1785.9955747862728]
In [68]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[68]:
[<matplotlib.lines.Line2D at 0x1e82ae84f98>]
K=6
In [69]:
kmeans_mfcc = KMeans(n_clusters=6, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[69]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [70]:
kmeans_mfcc.labels_
Out[70]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [71]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[71]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [72]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [73]:
X
Out[73]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 | 4 | 0 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 | 2 | 0 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 | 2 | 0 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 | 2 | 0 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 | 0 | 0 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 | 1 | 0 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 | 1 | 0 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 | 0 | 0 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 | 1 | 0 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 | 4 | 0 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 | 4 | 0 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 | 4 | 0 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 | 1 | 0 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 | 4 | 0 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 | 4 | 0 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 | 3 | 0 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 | 4 | 0 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 | 4 | 0 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 | 4 | 0 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 | 4 | 0 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 | 4 | 0 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 | 0 | 0 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 | 0 | 0 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 | 4 | 0 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 | 4 | 0 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 | 4 | 0 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 | 4 | 0 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 | 3 | 0 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 | 4 | 0 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 | 3 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 | 1 | 1 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 | 0 | 1 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 | 1 | 1 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 | 2 | 1 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 | 2 | 1 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 | 3 | 1 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 | 0 | 1 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 | 4 | 1 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 | 2 | 1 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 | 2 | 1 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 | 4 | 1 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 | 0 | 1 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 | 2 | 1 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 | 2 | 1 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 | 2 | 1 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 | 1 | 1 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 | 3 | 1 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 | 4 | 1 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 | 4 | 1 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 | 2 | 1 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 | 2 | 1 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 | 2 | 1 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 | 4 | 1 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 | 0 | 1 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 | 0 | 1 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 | 2 | 1 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 | 2 | 1 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 | 2 | 1 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 | 2 | 1 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 | 2 | 1 |
255 rows × 15 columns
In [74]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[74]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82aec1940>
In [207]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [208]:
X = df_n_ps_std_mfcc[2]
In [209]:
y = df_n_ps[2]['chosen']
In [210]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [211]:
X_train.shape
Out[211]:
(231, 13)
In [80]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [81]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [82]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [83]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.001, 'max_iter': 1000}, que permiten obtener un Accuracy de 84.42% y un Kappa del 45.84
Tiempo total: 27.81 minutos
In [212]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.001
epochs = 1000
In [213]:
input_tensor = Input(shape = (n0,))
In [214]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [215]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [216]:
model.summary()
Model: "model_8" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_8 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_23 (Dense) (None, 30) 420 _________________________________________________________________ dense_24 (Dense) (None, 1) 31 ================================================================= Total params: 451 Trainable params: 451 Non-trainable params: 0 _________________________________________________________________
In [217]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size= 32,
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 231 samples, validate on 78 samples Epoch 1/1000 231/231 [==============================] - 0s 653us/step - loss: 0.6856 - accuracy: 0.5628 - val_loss: 0.6899 - val_accuracy: 0.5769 Epoch 2/1000 231/231 [==============================] - 0s 52us/step - loss: 0.6619 - accuracy: 0.6147 - val_loss: 0.6783 - val_accuracy: 0.6154 Epoch 3/1000 231/231 [==============================] - 0s 52us/step - loss: 0.6436 - accuracy: 0.6623 - val_loss: 0.6683 - val_accuracy: 0.6026 Epoch 4/1000 231/231 [==============================] - 0s 82us/step - loss: 0.6261 - accuracy: 0.6840 - val_loss: 0.6598 - val_accuracy: 0.6154 Epoch 5/1000 231/231 [==============================] - 0s 56us/step - loss: 0.6105 - accuracy: 0.7273 - val_loss: 0.6508 - val_accuracy: 0.6282 Epoch 6/1000 231/231 [==============================] - 0s 56us/step - loss: 0.5965 - accuracy: 0.7489 - val_loss: 0.6424 - val_accuracy: 0.6154 Epoch 7/1000 231/231 [==============================] - 0s 56us/step - loss: 0.5832 - accuracy: 0.7576 - val_loss: 0.6345 - val_accuracy: 0.6410 Epoch 8/1000 231/231 [==============================] - 0s 52us/step - loss: 0.5711 - accuracy: 0.7532 - val_loss: 0.6278 - val_accuracy: 0.6667 Epoch 9/1000 231/231 [==============================] - 0s 52us/step - loss: 0.5596 - accuracy: 0.7446 - val_loss: 0.6198 - val_accuracy: 0.6667 Epoch 10/1000 231/231 [==============================] - 0s 56us/step - loss: 0.5479 - accuracy: 0.7619 - val_loss: 0.6118 - val_accuracy: 0.6923 Epoch 11/1000 231/231 [==============================] - 0s 65us/step - loss: 0.5373 - accuracy: 0.7662 - val_loss: 0.6053 - val_accuracy: 0.6923 Epoch 12/1000 231/231 [==============================] - 0s 56us/step - loss: 0.5275 - accuracy: 0.7706 - val_loss: 0.5994 - val_accuracy: 0.7051 Epoch 13/1000 231/231 [==============================] - 0s 69us/step - loss: 0.5175 - accuracy: 0.7749 - val_loss: 0.5939 - val_accuracy: 0.7179 Epoch 14/1000 231/231 [==============================] - 0s 69us/step - loss: 0.5082 - accuracy: 0.7922 - val_loss: 0.5909 - val_accuracy: 0.7051 Epoch 15/1000 231/231 [==============================] - 0s 82us/step - loss: 0.4998 - accuracy: 0.8009 - val_loss: 0.5871 - val_accuracy: 0.6795 Epoch 16/1000 231/231 [==============================] - 0s 95us/step - loss: 0.4915 - accuracy: 0.8052 - val_loss: 0.5853 - val_accuracy: 0.7051 Epoch 17/1000 231/231 [==============================] - 0s 104us/step - loss: 0.4826 - accuracy: 0.8052 - val_loss: 0.5799 - val_accuracy: 0.7179 Epoch 18/1000 231/231 [==============================] - 0s 91us/step - loss: 0.4751 - accuracy: 0.8095 - val_loss: 0.5749 - val_accuracy: 0.7179 Epoch 19/1000 231/231 [==============================] - 0s 91us/step - loss: 0.4677 - accuracy: 0.8052 - val_loss: 0.5729 - val_accuracy: 0.7179 Epoch 20/1000 231/231 [==============================] - 0s 78us/step - loss: 0.4604 - accuracy: 0.8052 - val_loss: 0.5686 - val_accuracy: 0.7179 Epoch 21/1000 231/231 [==============================] - 0s 87us/step - loss: 0.4529 - accuracy: 0.8139 - val_loss: 0.5653 - val_accuracy: 0.7308 Epoch 22/1000 231/231 [==============================] - 0s 82us/step - loss: 0.4467 - accuracy: 0.8268 - val_loss: 0.5641 - val_accuracy: 0.7179 Epoch 23/1000 231/231 [==============================] - 0s 65us/step - loss: 0.4392 - accuracy: 0.8398 - val_loss: 0.5616 - val_accuracy: 0.6923 Epoch 24/1000 231/231 [==============================] - 0s 56us/step - loss: 0.4332 - accuracy: 0.8571 - val_loss: 0.5597 - val_accuracy: 0.6923 Epoch 25/1000 231/231 [==============================] - 0s 61us/step - loss: 0.4273 - accuracy: 0.8571 - val_loss: 0.5587 - val_accuracy: 0.7051 Epoch 26/1000 231/231 [==============================] - 0s 69us/step - loss: 0.4217 - accuracy: 0.8571 - val_loss: 0.5558 - val_accuracy: 0.7051 Epoch 27/1000 231/231 [==============================] - 0s 65us/step - loss: 0.4169 - accuracy: 0.8615 - val_loss: 0.5539 - val_accuracy: 0.7051 Epoch 28/1000 231/231 [==============================] - 0s 56us/step - loss: 0.4122 - accuracy: 0.8485 - val_loss: 0.5520 - val_accuracy: 0.7179 Epoch 29/1000 231/231 [==============================] - 0s 56us/step - loss: 0.4079 - accuracy: 0.8571 - val_loss: 0.5550 - val_accuracy: 0.7051 Epoch 30/1000 231/231 [==============================] - 0s 61us/step - loss: 0.4040 - accuracy: 0.8571 - val_loss: 0.5576 - val_accuracy: 0.6923 Epoch 31/1000 231/231 [==============================] - 0s 56us/step - loss: 0.4000 - accuracy: 0.8615 - val_loss: 0.5584 - val_accuracy: 0.6923 Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 32/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3969 - accuracy: 0.8571 - val_loss: 0.5593 - val_accuracy: 0.6923 Epoch 33/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3953 - accuracy: 0.8615 - val_loss: 0.5589 - val_accuracy: 0.6923 Epoch 34/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3937 - accuracy: 0.8615 - val_loss: 0.5598 - val_accuracy: 0.6923 Epoch 35/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3923 - accuracy: 0.8571 - val_loss: 0.5604 - val_accuracy: 0.6923 Epoch 36/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3907 - accuracy: 0.8571 - val_loss: 0.5606 - val_accuracy: 0.6923 Epoch 37/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3892 - accuracy: 0.8528 - val_loss: 0.5614 - val_accuracy: 0.6923 Epoch 38/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3876 - accuracy: 0.8528 - val_loss: 0.5614 - val_accuracy: 0.6923 Epoch 39/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3861 - accuracy: 0.8485 - val_loss: 0.5616 - val_accuracy: 0.6923 Epoch 40/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3845 - accuracy: 0.8485 - val_loss: 0.5615 - val_accuracy: 0.6923 Epoch 41/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3831 - accuracy: 0.8528 - val_loss: 0.5625 - val_accuracy: 0.6795 Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 42/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3820 - accuracy: 0.8528 - val_loss: 0.5632 - val_accuracy: 0.6795 Epoch 43/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3814 - accuracy: 0.8528 - val_loss: 0.5635 - val_accuracy: 0.6795 Epoch 44/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3807 - accuracy: 0.8528 - val_loss: 0.5641 - val_accuracy: 0.6795 Epoch 45/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3802 - accuracy: 0.8571 - val_loss: 0.5642 - val_accuracy: 0.6923 Epoch 46/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3795 - accuracy: 0.8571 - val_loss: 0.5645 - val_accuracy: 0.6923 Epoch 47/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3789 - accuracy: 0.8571 - val_loss: 0.5648 - val_accuracy: 0.6923 Epoch 48/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3783 - accuracy: 0.8571 - val_loss: 0.5647 - val_accuracy: 0.7051 Epoch 49/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3776 - accuracy: 0.8571 - val_loss: 0.5655 - val_accuracy: 0.6923 Epoch 50/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3770 - accuracy: 0.8571 - val_loss: 0.5662 - val_accuracy: 0.6923 Epoch 51/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3764 - accuracy: 0.8571 - val_loss: 0.5668 - val_accuracy: 0.6923 Epoch 00051: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 52/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3759 - accuracy: 0.8571 - val_loss: 0.5670 - val_accuracy: 0.6923 Epoch 53/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3756 - accuracy: 0.8571 - val_loss: 0.5673 - val_accuracy: 0.6923 Epoch 54/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3753 - accuracy: 0.8571 - val_loss: 0.5675 - val_accuracy: 0.6923 Epoch 55/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3750 - accuracy: 0.8571 - val_loss: 0.5673 - val_accuracy: 0.6923 Epoch 56/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3747 - accuracy: 0.8571 - val_loss: 0.5673 - val_accuracy: 0.6923 Epoch 57/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3745 - accuracy: 0.8571 - val_loss: 0.5674 - val_accuracy: 0.6923 Epoch 58/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3742 - accuracy: 0.8528 - val_loss: 0.5674 - val_accuracy: 0.6923 Epoch 59/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3740 - accuracy: 0.8571 - val_loss: 0.5677 - val_accuracy: 0.6923 Epoch 60/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3736 - accuracy: 0.8528 - val_loss: 0.5680 - val_accuracy: 0.6923 Epoch 61/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3734 - accuracy: 0.8571 - val_loss: 0.5679 - val_accuracy: 0.6923 Epoch 00061: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 62/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3732 - accuracy: 0.8571 - val_loss: 0.5679 - val_accuracy: 0.6923 Epoch 63/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3731 - accuracy: 0.8571 - val_loss: 0.5680 - val_accuracy: 0.6923 Epoch 64/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3730 - accuracy: 0.8571 - val_loss: 0.5681 - val_accuracy: 0.6923 Epoch 65/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3728 - accuracy: 0.8571 - val_loss: 0.5680 - val_accuracy: 0.6923 Epoch 66/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3727 - accuracy: 0.8571 - val_loss: 0.5682 - val_accuracy: 0.6923 Epoch 67/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3726 - accuracy: 0.8571 - val_loss: 0.5684 - val_accuracy: 0.6923 Epoch 68/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3724 - accuracy: 0.8571 - val_loss: 0.5686 - val_accuracy: 0.6923 Epoch 69/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3723 - accuracy: 0.8571 - val_loss: 0.5686 - val_accuracy: 0.6923 Epoch 70/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3721 - accuracy: 0.8571 - val_loss: 0.5688 - val_accuracy: 0.6923 Epoch 71/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3720 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 00071: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 72/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3719 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 73/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3718 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 74/1000 231/231 [==============================] - 0s 121us/step - loss: 0.3718 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 75/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3717 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 76/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3716 - accuracy: 0.8571 - val_loss: 0.5689 - val_accuracy: 0.6923 Epoch 77/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3715 - accuracy: 0.8571 - val_loss: 0.5690 - val_accuracy: 0.6923 Epoch 78/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3715 - accuracy: 0.8571 - val_loss: 0.5692 - val_accuracy: 0.6923 Epoch 79/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3714 - accuracy: 0.8571 - val_loss: 0.5692 - val_accuracy: 0.6923 Epoch 80/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3714 - accuracy: 0.8571 - val_loss: 0.5693 - val_accuracy: 0.6923 Epoch 81/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3713 - accuracy: 0.8571 - val_loss: 0.5693 - val_accuracy: 0.6923 Epoch 00081: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 82/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3712 - accuracy: 0.8571 - val_loss: 0.5693 - val_accuracy: 0.6923 Epoch 83/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3712 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 84/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3712 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 85/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3711 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 86/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3711 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 87/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3711 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 88/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3711 - accuracy: 0.8571 - val_loss: 0.5694 - val_accuracy: 0.6923 Epoch 89/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3710 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 90/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3710 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 91/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 00091: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 92/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 93/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 94/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 95/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 96/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3709 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 97/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 98/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 99/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 100/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 101/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 00101: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 102/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 103/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 104/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3708 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 105/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 106/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 107/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 108/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 109/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 110/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 111/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 00111: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 112/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 113/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 114/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 115/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 116/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 117/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 118/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 119/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 120/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 121/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5695 - val_accuracy: 0.6923 Epoch 00121: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 122/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 123/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 124/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3707 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 125/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 126/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 127/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 128/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 129/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 130/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 131/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00131: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 132/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 133/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 134/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 135/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 136/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 137/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 138/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 139/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 140/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 141/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00141: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 142/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 143/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 144/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 145/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 146/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 147/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 148/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 149/1000 231/231 [==============================] - ETA: 0s - loss: 0.3757 - accuracy: 0.87 - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 150/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 151/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00151: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 152/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 153/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 154/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 155/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 156/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 157/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 158/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 159/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 160/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 161/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00161: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 162/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 163/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 164/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 165/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 166/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 167/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 168/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 169/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 170/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 171/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00171: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 172/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 173/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 174/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 175/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 176/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 177/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 178/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 179/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 180/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 181/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00181: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 182/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 183/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 184/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 185/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 186/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 187/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 188/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 189/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 190/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 191/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00191: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 192/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 193/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 194/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 195/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 196/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 197/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 198/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 199/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 200/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 201/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00201: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 202/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 203/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 204/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 205/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 206/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 207/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 208/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 209/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 210/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 211/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00211: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 212/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 213/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 214/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 215/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 216/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 217/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 218/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 219/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 220/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 221/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00221: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 222/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 223/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 224/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 225/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 226/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 227/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 228/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 229/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 230/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 231/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00231: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 232/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 233/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 234/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 235/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 236/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 237/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 238/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 239/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 240/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 241/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00241: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 242/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 243/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 244/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 245/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 246/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 247/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 248/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 249/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 250/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 251/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00251: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 252/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 253/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 254/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 255/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 256/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 257/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 258/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 259/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 260/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 261/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00261: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 262/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 263/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 264/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 265/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 266/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 267/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 268/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 269/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 270/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 271/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00271: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 272/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 273/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 274/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 275/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 276/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 277/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 278/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 279/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 280/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 281/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00281: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 282/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 283/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 284/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 285/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 286/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 287/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 288/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 289/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 290/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 291/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00291: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 292/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 293/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 294/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 295/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 296/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 297/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 298/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 299/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 300/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 301/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00301: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 302/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 303/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 304/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 305/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 306/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 307/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 308/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 309/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 310/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 311/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00311: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 312/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 313/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 314/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 315/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 316/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 317/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 318/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 319/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 320/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 321/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00321: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 322/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 323/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 324/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 325/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 326/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 327/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 328/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 329/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 330/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 331/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00331: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 332/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 333/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 334/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 335/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 336/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 337/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 338/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 339/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 340/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 341/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00341: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 342/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 343/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 344/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 345/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 346/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 347/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 348/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 349/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 350/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 351/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00351: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 352/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 353/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 354/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 355/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 356/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 357/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 358/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 359/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 360/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 361/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00361: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 362/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 363/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 364/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 365/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 366/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 367/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 368/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 369/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 370/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 371/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00371: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 372/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 373/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 374/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 375/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 376/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 377/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 378/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 379/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 380/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 381/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00381: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 382/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 383/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 384/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 385/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 386/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 387/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 388/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 389/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 390/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 391/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00391: ReduceLROnPlateau reducing learning rate to 7.275957959772868e-15. Epoch 392/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 393/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 394/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 395/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 396/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 397/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 398/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 399/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 400/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 401/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00401: ReduceLROnPlateau reducing learning rate to 3.637978979886434e-15. Epoch 402/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 403/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 404/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 405/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 406/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 407/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 408/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 409/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 410/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 411/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00411: ReduceLROnPlateau reducing learning rate to 1.818989489943217e-15. Epoch 412/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 413/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 414/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 415/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 416/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 417/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 418/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 419/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 420/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 421/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00421: ReduceLROnPlateau reducing learning rate to 9.094947449716085e-16. Epoch 422/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 423/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 424/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 425/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 426/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 427/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 428/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 429/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 430/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 431/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00431: ReduceLROnPlateau reducing learning rate to 4.547473724858043e-16. Epoch 432/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 433/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 434/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 435/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 436/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 437/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 438/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 439/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 440/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 441/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00441: ReduceLROnPlateau reducing learning rate to 2.2737368624290214e-16. Epoch 442/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 443/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 444/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 445/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 446/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 447/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 448/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 449/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 450/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 451/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00451: ReduceLROnPlateau reducing learning rate to 1.1368684312145107e-16. Epoch 452/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 453/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 454/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 455/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 456/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 457/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 458/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 459/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 460/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 461/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00461: ReduceLROnPlateau reducing learning rate to 5.684342156072553e-17. Epoch 462/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 463/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 464/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 465/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 466/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 467/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 468/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 469/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 470/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 471/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00471: ReduceLROnPlateau reducing learning rate to 2.842171078036277e-17. Epoch 472/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 473/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 474/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 475/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 476/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 477/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 478/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 479/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 480/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 481/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00481: ReduceLROnPlateau reducing learning rate to 1.4210855390181384e-17. Epoch 482/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 483/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 484/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 485/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 486/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 487/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 488/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 489/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 490/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 491/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00491: ReduceLROnPlateau reducing learning rate to 7.105427695090692e-18. Epoch 492/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 493/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 494/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 495/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 496/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 497/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 498/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 499/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 500/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 501/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00501: ReduceLROnPlateau reducing learning rate to 3.552713847545346e-18. Epoch 502/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 503/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 504/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 505/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 506/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 507/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 508/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 509/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 510/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 511/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00511: ReduceLROnPlateau reducing learning rate to 1.776356923772673e-18. Epoch 512/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 513/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 514/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 515/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 516/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 517/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 518/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 519/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 520/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 521/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00521: ReduceLROnPlateau reducing learning rate to 8.881784618863365e-19. Epoch 522/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 523/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 524/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 525/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 526/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 527/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 528/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 529/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 530/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 531/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00531: ReduceLROnPlateau reducing learning rate to 4.440892309431682e-19. Epoch 532/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 533/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 534/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 535/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 536/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 537/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 538/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 539/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 540/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 541/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00541: ReduceLROnPlateau reducing learning rate to 2.220446154715841e-19. Epoch 542/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 543/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 544/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 545/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 546/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 547/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 548/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 549/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 550/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 551/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00551: ReduceLROnPlateau reducing learning rate to 1.1102230773579206e-19. Epoch 552/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 553/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 554/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 555/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 556/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 557/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 558/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 559/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 560/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 561/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00561: ReduceLROnPlateau reducing learning rate to 5.551115386789603e-20. Epoch 562/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 563/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 564/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 565/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 566/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 567/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 568/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 569/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 570/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 571/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00571: ReduceLROnPlateau reducing learning rate to 2.7755576933948015e-20. Epoch 572/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 573/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 574/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 575/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 576/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 577/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 578/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 579/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 580/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 581/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00581: ReduceLROnPlateau reducing learning rate to 1.3877788466974007e-20. Epoch 582/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 583/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 584/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 585/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 586/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 587/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 588/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 589/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 590/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 591/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00591: ReduceLROnPlateau reducing learning rate to 6.938894233487004e-21. Epoch 592/1000 231/231 [==============================] - ETA: 0s - loss: 0.3154 - accuracy: 0.93 - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 593/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 594/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 595/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 596/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 597/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 598/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 599/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 600/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 601/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00601: ReduceLROnPlateau reducing learning rate to 3.469447116743502e-21. Epoch 602/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 603/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 604/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 605/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 606/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 607/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 608/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 609/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 610/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 611/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00611: ReduceLROnPlateau reducing learning rate to 1.734723558371751e-21. Epoch 612/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 613/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 614/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 615/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 616/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 617/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 618/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 619/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 620/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 621/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00621: ReduceLROnPlateau reducing learning rate to 8.673617791858755e-22. Epoch 622/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 623/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 624/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 625/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 626/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 627/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 628/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 629/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 630/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 631/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00631: ReduceLROnPlateau reducing learning rate to 4.336808895929377e-22. Epoch 632/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 633/1000 231/231 [==============================] - 0s 48us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 634/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 635/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 636/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 637/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 638/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 639/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 640/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 641/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00641: ReduceLROnPlateau reducing learning rate to 2.1684044479646887e-22. Epoch 642/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 643/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 644/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 645/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 646/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 647/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 648/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 649/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 650/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 651/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00651: ReduceLROnPlateau reducing learning rate to 1.0842022239823443e-22. Epoch 652/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 653/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 654/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 655/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 656/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 657/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 658/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 659/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 660/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 661/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00661: ReduceLROnPlateau reducing learning rate to 5.421011119911722e-23. Epoch 662/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 663/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 664/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 665/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 666/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 667/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 668/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 669/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 670/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 671/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00671: ReduceLROnPlateau reducing learning rate to 2.710505559955861e-23. Epoch 672/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 673/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 674/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 675/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 676/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 677/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 678/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 679/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 680/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 681/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00681: ReduceLROnPlateau reducing learning rate to 1.3552527799779304e-23. Epoch 682/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 683/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 684/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 685/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 686/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 687/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 688/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 689/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 690/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 691/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00691: ReduceLROnPlateau reducing learning rate to 6.776263899889652e-24. Epoch 692/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 693/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 694/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 695/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 696/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 697/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 698/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 699/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 700/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 701/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00701: ReduceLROnPlateau reducing learning rate to 3.388131949944826e-24. Epoch 702/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 703/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 704/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 705/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 706/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 707/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 708/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 709/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 710/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 711/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00711: ReduceLROnPlateau reducing learning rate to 1.694065974972413e-24. Epoch 712/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 713/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 714/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 715/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 716/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 717/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 718/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 719/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 720/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 721/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00721: ReduceLROnPlateau reducing learning rate to 8.470329874862065e-25. Epoch 722/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 723/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 724/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 725/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 726/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 727/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 728/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 729/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 730/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 731/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00731: ReduceLROnPlateau reducing learning rate to 4.2351649374310325e-25. Epoch 732/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 733/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 734/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 735/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 736/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 737/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 738/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 739/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 740/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 741/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00741: ReduceLROnPlateau reducing learning rate to 2.1175824687155163e-25. Epoch 742/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 743/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 744/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 745/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 746/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 747/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 748/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 749/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 750/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 751/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00751: ReduceLROnPlateau reducing learning rate to 1.0587912343577581e-25. Epoch 752/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 753/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 754/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 755/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 756/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 757/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 758/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 759/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 760/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 761/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00761: ReduceLROnPlateau reducing learning rate to 5.293956171788791e-26. Epoch 762/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 763/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 764/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 765/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 766/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 767/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 768/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 769/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 770/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 771/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00771: ReduceLROnPlateau reducing learning rate to 2.6469780858943953e-26. Epoch 772/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 773/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 774/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 775/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 776/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 777/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 778/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 779/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 780/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 781/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00781: ReduceLROnPlateau reducing learning rate to 1.3234890429471977e-26. Epoch 782/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 783/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 784/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 785/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 786/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 787/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 788/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 789/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 790/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 791/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00791: ReduceLROnPlateau reducing learning rate to 6.617445214735988e-27. Epoch 792/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 793/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 794/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 795/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 796/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 797/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 798/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 799/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 800/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 801/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00801: ReduceLROnPlateau reducing learning rate to 3.308722607367994e-27. Epoch 802/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 803/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 804/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 805/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 806/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 807/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 808/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 809/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 810/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 811/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00811: ReduceLROnPlateau reducing learning rate to 1.654361303683997e-27. Epoch 812/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 813/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 814/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 815/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 816/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 817/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 818/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 819/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 820/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 821/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00821: ReduceLROnPlateau reducing learning rate to 8.271806518419985e-28. Epoch 822/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 823/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 824/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 825/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 826/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 827/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 828/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 829/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 830/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 831/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00831: ReduceLROnPlateau reducing learning rate to 4.135903259209993e-28. Epoch 832/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 833/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 834/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 835/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 836/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 837/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 838/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 839/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 840/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 841/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00841: ReduceLROnPlateau reducing learning rate to 2.0679516296049964e-28. Epoch 842/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 843/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 844/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 845/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 846/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 847/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 848/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 849/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 850/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 851/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00851: ReduceLROnPlateau reducing learning rate to 1.0339758148024982e-28. Epoch 852/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 853/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 854/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 855/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 856/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 857/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 858/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 859/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 860/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 861/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00861: ReduceLROnPlateau reducing learning rate to 5.169879074012491e-29. Epoch 862/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 863/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 864/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 865/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 866/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 867/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 868/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 869/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 870/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 871/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00871: ReduceLROnPlateau reducing learning rate to 2.5849395370062454e-29. Epoch 872/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 873/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 874/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 875/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 876/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 877/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 878/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 879/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 880/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 881/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00881: ReduceLROnPlateau reducing learning rate to 1.2924697685031227e-29. Epoch 882/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 883/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 884/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 885/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 886/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 887/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 888/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 889/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 890/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 891/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00891: ReduceLROnPlateau reducing learning rate to 6.462348842515614e-30. Epoch 892/1000 231/231 [==============================] - 0s 48us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 893/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 894/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 895/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 896/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 897/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 898/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 899/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 900/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 901/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00901: ReduceLROnPlateau reducing learning rate to 3.231174421257807e-30. Epoch 902/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 903/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 904/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 905/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 906/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 907/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 908/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 909/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 910/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 911/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00911: ReduceLROnPlateau reducing learning rate to 1.6155872106289034e-30. Epoch 912/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 913/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 914/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 915/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 916/1000 231/231 [==============================] - 0s 52us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 917/1000 231/231 [==============================] - 0s 56us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 918/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 919/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 920/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 921/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00921: ReduceLROnPlateau reducing learning rate to 8.077936053144517e-31. Epoch 922/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 923/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 924/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 925/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 926/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 927/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 928/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 929/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 930/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 931/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00931: ReduceLROnPlateau reducing learning rate to 4.0389680265722585e-31. Epoch 932/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 933/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 934/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 935/1000 231/231 [==============================] - 0s 74us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 936/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 937/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 938/1000 231/231 [==============================] - 0s 112us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 939/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 940/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 941/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00941: ReduceLROnPlateau reducing learning rate to 2.0194840132861292e-31. Epoch 942/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 943/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 944/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 945/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 946/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 947/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 948/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 949/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 950/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 951/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00951: ReduceLROnPlateau reducing learning rate to 1.0097420066430646e-31. Epoch 952/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 953/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 954/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 955/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 956/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 957/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 958/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 959/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 960/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 961/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00961: ReduceLROnPlateau reducing learning rate to 5.048710033215323e-32. Epoch 962/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 963/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 964/1000 231/231 [==============================] - 0s 125us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 965/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 966/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 967/1000 231/231 [==============================] - 0s 121us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 968/1000 231/231 [==============================] - 0s 121us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 969/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 970/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 971/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00971: ReduceLROnPlateau reducing learning rate to 2.5243550166076616e-32. Epoch 972/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 973/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 974/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 975/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 976/1000 231/231 [==============================] - 0s 112us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 977/1000 231/231 [==============================] - 0s 121us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 978/1000 231/231 [==============================] - 0s 112us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 979/1000 231/231 [==============================] - 0s 112us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 980/1000 231/231 [==============================] - 0s 108us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 981/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00981: ReduceLROnPlateau reducing learning rate to 1.2621775083038308e-32. Epoch 982/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 983/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 984/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 985/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 986/1000 231/231 [==============================] - 0s 82us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 987/1000 231/231 [==============================] - 0s 87us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 988/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 989/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 990/1000 231/231 [==============================] - 0s 91us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 991/1000 231/231 [==============================] - 0s 121us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 00991: ReduceLROnPlateau reducing learning rate to 6.310887541519154e-33. Epoch 992/1000 231/231 [==============================] - 0s 104us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 993/1000 231/231 [==============================] - 0s 95us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 994/1000 231/231 [==============================] - 0s 100us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 995/1000 231/231 [==============================] - 0s 78us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 996/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 997/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 998/1000 231/231 [==============================] - 0s 65us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 999/1000 231/231 [==============================] - 0s 69us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923 Epoch 1000/1000 231/231 [==============================] - 0s 61us/step - loss: 0.3706 - accuracy: 0.8571 - val_loss: 0.5696 - val_accuracy: 0.6923
In [218]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 1000)
In [219]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
78/78 [==============================] - 0s 51us/step test loss: 0.5695895949999491, test accuracy: 0.692307710647583
In [220]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6775271512113618
In [221]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.009523809523809601
KMeans¶
In [93]:
X
Out[93]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.784459 | 0.109342 | 0.681608 | 1.151904 | -0.707724 | 0.736365 | 0.241404 | -0.461605 | 1.084621 | 0.123497 | -0.150398 | 1.784532 | 0.824544 |
| 1 | 0.213444 | 0.453851 | 0.215705 | 0.987439 | -1.851748 | -0.001814 | -0.218203 | 0.548263 | -0.521851 | 1.253720 | 0.882518 | -0.892913 | 0.218184 |
| 2 | 1.478029 | 0.664210 | 0.484232 | 0.450284 | -0.427587 | -0.448079 | -0.195272 | -0.610679 | -0.675633 | 0.759098 | -0.107303 | -0.440790 | -1.398093 |
| 3 | -0.846386 | -0.742706 | -0.734786 | -0.199585 | -0.328948 | -0.230911 | 0.620910 | 0.911236 | 1.274407 | 0.150882 | -0.603865 | -0.919849 | 0.386069 |
| 4 | -0.952033 | -0.794706 | -1.141199 | -0.070979 | 0.454453 | 0.544600 | 1.427005 | 1.918539 | 1.204102 | 0.076961 | -0.328712 | -1.051776 | -0.151007 |
| 5 | -1.244473 | 2.250723 | 2.321007 | 0.549219 | 1.971653 | -1.427849 | 0.392314 | -1.015093 | 0.157566 | 0.486970 | -0.455070 | 0.229936 | 0.185742 |
| 6 | -0.128652 | 0.958605 | 1.191477 | 0.705974 | 0.793937 | -0.709130 | -0.221572 | 0.922219 | 0.500475 | -0.571099 | 0.521526 | 0.567361 | -0.486761 |
| 7 | -1.148662 | 0.562286 | 0.636059 | 0.222732 | 0.741470 | 0.009108 | -0.949365 | -0.486631 | -0.519627 | -0.739600 | -0.415884 | -0.026850 | 0.290685 |
| 8 | -0.440190 | 0.461355 | -0.016542 | -0.158636 | 0.985626 | -0.417310 | 0.182134 | 0.290631 | 0.379949 | 0.069159 | 1.080013 | 0.220566 | -0.042505 |
| 9 | -0.695730 | 0.398404 | 1.069978 | -0.124019 | 0.736597 | -0.912452 | 0.673156 | 0.604840 | 0.175505 | 0.496158 | 0.541788 | 0.649837 | -0.680054 |
| 10 | -0.006662 | 0.163832 | 1.373872 | -0.095120 | 1.621755 | 1.048509 | 0.997122 | 0.721763 | 0.660834 | -1.076324 | 0.925997 | -0.147393 | -0.420465 |
| 11 | -0.771763 | -0.484525 | -0.874411 | 0.647747 | -1.241650 | 0.190918 | 0.457290 | 0.915208 | 1.999689 | 1.879761 | 0.491598 | -0.164372 | -0.560754 |
| 12 | 0.140770 | 1.869847 | -1.926303 | -2.491201 | -2.679759 | -1.527330 | -0.299345 | -0.550878 | 0.702947 | 0.143961 | 0.034796 | -0.379551 | -0.422354 |
| 13 | -1.952477 | -0.949813 | 0.063314 | 1.188657 | 1.059601 | 1.221319 | 0.070346 | 2.284107 | 2.889527 | 2.012105 | 1.053494 | -0.178905 | -2.004333 |
| 14 | -0.895529 | 0.398850 | -0.469782 | 1.216393 | 0.657294 | -0.550619 | -0.854637 | -0.815454 | 1.929689 | 1.499328 | -0.096775 | -0.174183 | -1.119396 |
| 15 | -1.161372 | 1.475106 | 1.486594 | 0.127516 | 0.213940 | 0.587080 | -0.789652 | 0.130203 | 1.199389 | 1.458358 | 0.404206 | 0.754289 | -0.784214 |
| 16 | -0.476792 | 2.179287 | 0.101035 | -1.393755 | -0.740834 | 0.589666 | 0.873850 | 0.630539 | 0.535702 | 0.387326 | -0.979677 | 0.259755 | 0.313358 |
| 17 | -0.089088 | -0.841832 | 0.674093 | -0.842623 | 0.904577 | -1.476862 | 1.853427 | -1.108621 | 0.720923 | 0.383320 | -1.842030 | 1.712321 | -1.612726 |
| 18 | -1.772732 | 0.488101 | 0.057829 | 0.041074 | 0.732429 | 1.052187 | 0.279830 | -0.350521 | -0.476338 | -0.833438 | 0.184849 | -0.055428 | 0.627307 |
| 19 | -0.640351 | 0.068493 | 0.619966 | -0.599171 | 0.860806 | -0.385120 | 1.955087 | -1.014740 | 1.224043 | 1.450896 | -2.604448 | 2.187869 | -0.464774 |
| 20 | 0.590240 | 0.699904 | -0.097902 | 0.127319 | -0.882999 | 0.319144 | -0.146142 | -0.540616 | 0.300593 | 0.688863 | 0.314647 | 0.709538 | 0.572811 |
| 21 | 0.500240 | 0.875222 | -0.833826 | 0.377484 | 0.023480 | 1.321472 | 1.094037 | 0.734507 | 0.141947 | 0.214524 | 0.508556 | -0.265911 | -0.372316 |
| 22 | -0.076653 | 0.518030 | 0.003390 | 0.452969 | -0.218736 | 0.115409 | 0.332618 | 0.611098 | 0.211893 | -0.206368 | 0.358363 | 0.614915 | 0.518172 |
| 23 | 0.010763 | -0.352873 | -0.460051 | 0.423968 | -0.228393 | -0.040296 | -0.740869 | -0.810034 | -1.379366 | -0.179024 | 0.147810 | -0.224826 | 0.615011 |
| 24 | 0.874600 | 0.173728 | -1.041125 | 0.845285 | 1.139221 | 0.264458 | -0.378878 | 0.430226 | -0.568469 | -1.237333 | 0.032074 | 0.812111 | 0.431460 |
| 25 | 0.200637 | 0.337376 | 0.022126 | 1.189135 | -0.210135 | -1.195492 | 0.067874 | 1.349711 | -0.534365 | -0.132754 | 0.055132 | 0.239009 | -0.275633 |
| 26 | 0.362627 | 0.159292 | -1.211688 | -0.555502 | 0.107540 | 0.797027 | -0.246321 | -1.113565 | -1.373054 | -2.369077 | -0.539483 | 1.032005 | 1.637730 |
| 27 | -0.504648 | -0.561515 | -2.173809 | -1.525691 | -0.810132 | -0.617474 | 0.441103 | 1.146056 | 1.464488 | -1.111032 | -0.742722 | 0.034623 | 0.200147 |
| 28 | -0.339646 | -2.140319 | -1.409226 | -0.207553 | -1.216547 | -1.135346 | -0.831817 | 1.136334 | -0.187159 | 1.388841 | 0.282573 | -0.807850 | -0.371992 |
| 29 | -1.279089 | 1.555887 | 0.890503 | 2.134195 | 0.337580 | -0.037382 | -2.046955 | -2.888113 | 1.329665 | 1.436687 | -1.576201 | 0.485256 | 1.429246 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 279 | -1.132789 | -0.931481 | -0.350024 | -0.228575 | -1.201208 | -1.044342 | 0.532403 | 1.667036 | 1.383485 | -0.967474 | -0.286625 | -1.920618 | -0.797190 |
| 280 | -0.375948 | 0.058369 | 0.489068 | 0.862825 | -1.876102 | -0.195043 | -1.163295 | 0.716190 | 0.384576 | -0.168340 | 1.542126 | -0.769460 | 0.456686 |
| 281 | 0.412883 | -1.703432 | -0.514845 | -1.382818 | -0.713972 | -0.476089 | 1.471006 | 0.826485 | 0.508608 | -1.311788 | -2.010635 | -1.122699 | -0.848851 |
| 282 | -0.152329 | -2.012108 | -0.217355 | -1.122627 | -0.851075 | 0.634424 | 1.711007 | 0.281350 | -0.565156 | -1.667195 | -1.942452 | -1.586592 | -0.485128 |
| 283 | 0.348443 | -2.381428 | 1.267515 | -1.713290 | 0.161262 | -1.589515 | 1.383857 | -0.218429 | 0.412550 | 0.382171 | -1.073499 | -1.745128 | -3.227845 |
| 284 | -0.895866 | 1.001673 | 1.059356 | 0.166883 | -0.710729 | 0.466737 | -0.857566 | -0.158962 | 0.004241 | 0.391823 | 0.576231 | 0.329506 | -1.331272 |
| 285 | 0.417102 | 1.957515 | 2.350604 | -1.125042 | -2.206390 | -0.674814 | -1.217854 | 0.372865 | 0.840465 | -0.472910 | 0.310419 | 1.379494 | 1.128412 |
| 286 | -0.900897 | -0.289100 | 0.433265 | -0.281829 | -0.379951 | 1.272236 | 0.313949 | -0.261980 | -0.053111 | 0.473694 | 0.493962 | -0.263293 | -0.657598 |
| 287 | -0.002448 | -0.853612 | 0.441903 | 0.406478 | -0.823085 | 0.590185 | -0.292046 | -0.079952 | -0.422138 | 0.579522 | -0.620415 | -0.298847 | 0.620798 |
| 288 | -0.528092 | -1.022206 | -0.348679 | 0.093718 | -1.642833 | -2.355166 | -0.992806 | -0.143423 | 0.270521 | 0.838321 | 0.843686 | 0.469574 | -0.325121 |
| 289 | -0.387248 | -1.305014 | -0.365540 | 0.202745 | -0.906016 | -1.785190 | -1.377992 | -0.544742 | -0.670979 | -0.785606 | 0.505505 | 0.502505 | -0.151297 |
| 290 | 0.425324 | -2.583173 | -2.181080 | -1.262030 | -0.179265 | 0.176164 | 1.763096 | 0.436737 | -2.048534 | -1.014266 | 1.298221 | 0.401742 | -1.080608 |
| 291 | -0.572282 | -0.375532 | -2.067885 | -0.361247 | -0.315065 | -0.671820 | -0.183865 | -0.517694 | -0.802956 | -0.951809 | 0.282442 | 0.208005 | -0.271252 |
| 292 | -0.084382 | -1.508230 | -0.105496 | -1.930204 | -1.529664 | -0.795467 | 1.273717 | -1.858542 | -0.446361 | -0.239346 | 0.154464 | -0.114937 | -1.831603 |
| 293 | -1.172703 | 0.783209 | -1.141589 | -0.982768 | -0.513216 | 0.655437 | 1.962510 | 0.628858 | 1.130028 | 1.104741 | 1.539591 | 1.547843 | -0.011302 |
| 294 | -1.293038 | 0.838303 | -1.049071 | -0.708031 | -0.779995 | 0.868108 | 1.621994 | 0.725495 | 1.173585 | 1.424395 | 1.751950 | 1.352876 | 0.339922 |
| 295 | 0.809878 | -0.351504 | -2.231752 | -0.556719 | -1.430264 | -0.357918 | -0.727837 | 1.110363 | 1.684188 | 0.429768 | 0.560061 | 0.371789 | -1.110030 |
| 296 | 0.305449 | -0.148924 | -0.727054 | -0.126830 | 0.467272 | 0.420013 | 1.212777 | 0.954055 | -0.988419 | -0.423614 | -0.047239 | 0.058678 | -0.031517 |
| 297 | 1.212224 | 1.916789 | 0.287969 | -0.073842 | 0.289112 | 0.943764 | -0.395404 | -0.380613 | 0.262567 | 0.759137 | 0.277177 | 0.493951 | 1.026995 |
| 298 | -0.121307 | 0.217217 | 0.030920 | -0.201270 | -0.752001 | -0.276070 | 0.835502 | -0.363704 | -0.641199 | 0.283313 | 0.060013 | 0.013280 | 0.477857 |
| 299 | -0.622824 | -0.595352 | 0.256282 | -0.111551 | 0.023990 | 1.221659 | 1.572998 | -0.263983 | -0.707828 | 0.707801 | 0.306249 | 1.046476 | 0.214979 |
| 300 | -0.667480 | -0.808638 | 0.730781 | 0.054549 | 0.191421 | 0.279885 | 0.088177 | 0.823617 | 0.604299 | 0.640274 | -0.360151 | 1.298688 | 0.494875 |
| 301 | 0.928382 | -2.375767 | -0.427528 | -0.852350 | -1.137004 | 1.584181 | -1.700220 | -2.060965 | -1.326622 | 0.451948 | 0.593212 | 0.152418 | -0.128797 |
| 302 | -0.483888 | 0.443846 | 0.129714 | 0.199624 | -0.106985 | 0.817702 | -0.072817 | -1.163918 | 0.545762 | -0.141320 | 0.041767 | -0.402181 | 0.061897 |
| 303 | 0.715769 | 0.780533 | 1.467750 | -0.595580 | -1.178484 | 4.014345 | -0.112339 | -1.611382 | -0.295511 | 0.032462 | 1.836607 | -4.315898 | -1.084441 |
| 304 | 0.041466 | -0.470275 | 0.234655 | 0.109532 | -0.518455 | -0.977540 | -0.613498 | -1.108545 | 0.500653 | -0.214143 | -0.033265 | -0.541673 | 0.714974 |
| 305 | 0.818747 | 0.495675 | 1.005686 | 0.967334 | 0.505171 | -0.579478 | -0.847677 | 1.574323 | 1.544556 | 0.412556 | -0.972040 | 0.290457 | 0.289042 |
| 306 | 1.062928 | -1.149587 | 1.951840 | -0.065775 | 0.546680 | 0.994901 | -1.817826 | 2.109742 | 0.264443 | 0.505287 | -0.757462 | 0.578677 | 0.222503 |
| 307 | -0.701621 | -0.049803 | -0.719153 | -0.048069 | 1.223251 | 1.913492 | 0.887449 | 0.038186 | 0.546172 | -0.568362 | -1.091833 | -0.250367 | 0.831399 |
| 308 | -0.079821 | 0.796085 | -0.215763 | -1.396439 | -0.133350 | 0.582037 | 2.442796 | 0.743250 | -1.182753 | -0.723658 | -0.879934 | -2.498899 | -1.532262 |
309 rows × 13 columns
In [94]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[94]:
[4016.9999999999995, 3599.8293806720085, 3349.349727264702, 3159.6428991584926, 3002.905895600155, 2909.3645052598604, 2804.695882234172, 2720.275460001156, 2622.695881163609, 2543.6022931320426, 2484.176525692807, 2436.6681239209124, 2402.363548718592, 2338.201438573343]
In [95]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[95]:
[<matplotlib.lines.Line2D at 0x1e82b4d8dd8>]
K=2
In [96]:
kmeans_mfcc = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[96]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [97]:
kmeans_mfcc.labels_
Out[97]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,
0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,
0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0])
In [98]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[98]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,
0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,
0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0])
In [99]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [100]:
X
Out[100]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.784459 | 0.109342 | 0.681608 | 1.151904 | -0.707724 | 0.736365 | 0.241404 | -0.461605 | 1.084621 | 0.123497 | -0.150398 | 1.784532 | 0.824544 | 1 | 0 |
| 1 | 0.213444 | 0.453851 | 0.215705 | 0.987439 | -1.851748 | -0.001814 | -0.218203 | 0.548263 | -0.521851 | 1.253720 | 0.882518 | -0.892913 | 0.218184 | 1 | 0 |
| 2 | 1.478029 | 0.664210 | 0.484232 | 0.450284 | -0.427587 | -0.448079 | -0.195272 | -0.610679 | -0.675633 | 0.759098 | -0.107303 | -0.440790 | -1.398093 | 0 | 0 |
| 3 | -0.846386 | -0.742706 | -0.734786 | -0.199585 | -0.328948 | -0.230911 | 0.620910 | 0.911236 | 1.274407 | 0.150882 | -0.603865 | -0.919849 | 0.386069 | 0 | 0 |
| 4 | -0.952033 | -0.794706 | -1.141199 | -0.070979 | 0.454453 | 0.544600 | 1.427005 | 1.918539 | 1.204102 | 0.076961 | -0.328712 | -1.051776 | -0.151007 | 0 | 0 |
| 5 | -1.244473 | 2.250723 | 2.321007 | 0.549219 | 1.971653 | -1.427849 | 0.392314 | -1.015093 | 0.157566 | 0.486970 | -0.455070 | 0.229936 | 0.185742 | 1 | 0 |
| 6 | -0.128652 | 0.958605 | 1.191477 | 0.705974 | 0.793937 | -0.709130 | -0.221572 | 0.922219 | 0.500475 | -0.571099 | 0.521526 | 0.567361 | -0.486761 | 1 | 0 |
| 7 | -1.148662 | 0.562286 | 0.636059 | 0.222732 | 0.741470 | 0.009108 | -0.949365 | -0.486631 | -0.519627 | -0.739600 | -0.415884 | -0.026850 | 0.290685 | 1 | 0 |
| 8 | -0.440190 | 0.461355 | -0.016542 | -0.158636 | 0.985626 | -0.417310 | 0.182134 | 0.290631 | 0.379949 | 0.069159 | 1.080013 | 0.220566 | -0.042505 | 1 | 0 |
| 9 | -0.695730 | 0.398404 | 1.069978 | -0.124019 | 0.736597 | -0.912452 | 0.673156 | 0.604840 | 0.175505 | 0.496158 | 0.541788 | 0.649837 | -0.680054 | 1 | 0 |
| 10 | -0.006662 | 0.163832 | 1.373872 | -0.095120 | 1.621755 | 1.048509 | 0.997122 | 0.721763 | 0.660834 | -1.076324 | 0.925997 | -0.147393 | -0.420465 | 1 | 0 |
| 11 | -0.771763 | -0.484525 | -0.874411 | 0.647747 | -1.241650 | 0.190918 | 0.457290 | 0.915208 | 1.999689 | 1.879761 | 0.491598 | -0.164372 | -0.560754 | 1 | 0 |
| 12 | 0.140770 | 1.869847 | -1.926303 | -2.491201 | -2.679759 | -1.527330 | -0.299345 | -0.550878 | 0.702947 | 0.143961 | 0.034796 | -0.379551 | -0.422354 | 0 | 0 |
| 13 | -1.952477 | -0.949813 | 0.063314 | 1.188657 | 1.059601 | 1.221319 | 0.070346 | 2.284107 | 2.889527 | 2.012105 | 1.053494 | -0.178905 | -2.004333 | 1 | 0 |
| 14 | -0.895529 | 0.398850 | -0.469782 | 1.216393 | 0.657294 | -0.550619 | -0.854637 | -0.815454 | 1.929689 | 1.499328 | -0.096775 | -0.174183 | -1.119396 | 1 | 0 |
| 15 | -1.161372 | 1.475106 | 1.486594 | 0.127516 | 0.213940 | 0.587080 | -0.789652 | 0.130203 | 1.199389 | 1.458358 | 0.404206 | 0.754289 | -0.784214 | 1 | 0 |
| 16 | -0.476792 | 2.179287 | 0.101035 | -1.393755 | -0.740834 | 0.589666 | 0.873850 | 0.630539 | 0.535702 | 0.387326 | -0.979677 | 0.259755 | 0.313358 | 1 | 0 |
| 17 | -0.089088 | -0.841832 | 0.674093 | -0.842623 | 0.904577 | -1.476862 | 1.853427 | -1.108621 | 0.720923 | 0.383320 | -1.842030 | 1.712321 | -1.612726 | 0 | 0 |
| 18 | -1.772732 | 0.488101 | 0.057829 | 0.041074 | 0.732429 | 1.052187 | 0.279830 | -0.350521 | -0.476338 | -0.833438 | 0.184849 | -0.055428 | 0.627307 | 1 | 0 |
| 19 | -0.640351 | 0.068493 | 0.619966 | -0.599171 | 0.860806 | -0.385120 | 1.955087 | -1.014740 | 1.224043 | 1.450896 | -2.604448 | 2.187869 | -0.464774 | 1 | 0 |
| 20 | 0.590240 | 0.699904 | -0.097902 | 0.127319 | -0.882999 | 0.319144 | -0.146142 | -0.540616 | 0.300593 | 0.688863 | 0.314647 | 0.709538 | 0.572811 | 1 | 0 |
| 21 | 0.500240 | 0.875222 | -0.833826 | 0.377484 | 0.023480 | 1.321472 | 1.094037 | 0.734507 | 0.141947 | 0.214524 | 0.508556 | -0.265911 | -0.372316 | 1 | 0 |
| 22 | -0.076653 | 0.518030 | 0.003390 | 0.452969 | -0.218736 | 0.115409 | 0.332618 | 0.611098 | 0.211893 | -0.206368 | 0.358363 | 0.614915 | 0.518172 | 1 | 0 |
| 23 | 0.010763 | -0.352873 | -0.460051 | 0.423968 | -0.228393 | -0.040296 | -0.740869 | -0.810034 | -1.379366 | -0.179024 | 0.147810 | -0.224826 | 0.615011 | 1 | 0 |
| 24 | 0.874600 | 0.173728 | -1.041125 | 0.845285 | 1.139221 | 0.264458 | -0.378878 | 0.430226 | -0.568469 | -1.237333 | 0.032074 | 0.812111 | 0.431460 | 1 | 0 |
| 25 | 0.200637 | 0.337376 | 0.022126 | 1.189135 | -0.210135 | -1.195492 | 0.067874 | 1.349711 | -0.534365 | -0.132754 | 0.055132 | 0.239009 | -0.275633 | 1 | 0 |
| 26 | 0.362627 | 0.159292 | -1.211688 | -0.555502 | 0.107540 | 0.797027 | -0.246321 | -1.113565 | -1.373054 | -2.369077 | -0.539483 | 1.032005 | 1.637730 | 1 | 0 |
| 27 | -0.504648 | -0.561515 | -2.173809 | -1.525691 | -0.810132 | -0.617474 | 0.441103 | 1.146056 | 1.464488 | -1.111032 | -0.742722 | 0.034623 | 0.200147 | 0 | 0 |
| 28 | -0.339646 | -2.140319 | -1.409226 | -0.207553 | -1.216547 | -1.135346 | -0.831817 | 1.136334 | -0.187159 | 1.388841 | 0.282573 | -0.807850 | -0.371992 | 0 | 0 |
| 29 | -1.279089 | 1.555887 | 0.890503 | 2.134195 | 0.337580 | -0.037382 | -2.046955 | -2.888113 | 1.329665 | 1.436687 | -1.576201 | 0.485256 | 1.429246 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 279 | -1.132789 | -0.931481 | -0.350024 | -0.228575 | -1.201208 | -1.044342 | 0.532403 | 1.667036 | 1.383485 | -0.967474 | -0.286625 | -1.920618 | -0.797190 | 0 | 1 |
| 280 | -0.375948 | 0.058369 | 0.489068 | 0.862825 | -1.876102 | -0.195043 | -1.163295 | 0.716190 | 0.384576 | -0.168340 | 1.542126 | -0.769460 | 0.456686 | 1 | 1 |
| 281 | 0.412883 | -1.703432 | -0.514845 | -1.382818 | -0.713972 | -0.476089 | 1.471006 | 0.826485 | 0.508608 | -1.311788 | -2.010635 | -1.122699 | -0.848851 | 0 | 1 |
| 282 | -0.152329 | -2.012108 | -0.217355 | -1.122627 | -0.851075 | 0.634424 | 1.711007 | 0.281350 | -0.565156 | -1.667195 | -1.942452 | -1.586592 | -0.485128 | 0 | 1 |
| 283 | 0.348443 | -2.381428 | 1.267515 | -1.713290 | 0.161262 | -1.589515 | 1.383857 | -0.218429 | 0.412550 | 0.382171 | -1.073499 | -1.745128 | -3.227845 | 0 | 1 |
| 284 | -0.895866 | 1.001673 | 1.059356 | 0.166883 | -0.710729 | 0.466737 | -0.857566 | -0.158962 | 0.004241 | 0.391823 | 0.576231 | 0.329506 | -1.331272 | 1 | 1 |
| 285 | 0.417102 | 1.957515 | 2.350604 | -1.125042 | -2.206390 | -0.674814 | -1.217854 | 0.372865 | 0.840465 | -0.472910 | 0.310419 | 1.379494 | 1.128412 | 1 | 1 |
| 286 | -0.900897 | -0.289100 | 0.433265 | -0.281829 | -0.379951 | 1.272236 | 0.313949 | -0.261980 | -0.053111 | 0.473694 | 0.493962 | -0.263293 | -0.657598 | 1 | 1 |
| 287 | -0.002448 | -0.853612 | 0.441903 | 0.406478 | -0.823085 | 0.590185 | -0.292046 | -0.079952 | -0.422138 | 0.579522 | -0.620415 | -0.298847 | 0.620798 | 1 | 1 |
| 288 | -0.528092 | -1.022206 | -0.348679 | 0.093718 | -1.642833 | -2.355166 | -0.992806 | -0.143423 | 0.270521 | 0.838321 | 0.843686 | 0.469574 | -0.325121 | 0 | 1 |
| 289 | -0.387248 | -1.305014 | -0.365540 | 0.202745 | -0.906016 | -1.785190 | -1.377992 | -0.544742 | -0.670979 | -0.785606 | 0.505505 | 0.502505 | -0.151297 | 0 | 1 |
| 290 | 0.425324 | -2.583173 | -2.181080 | -1.262030 | -0.179265 | 0.176164 | 1.763096 | 0.436737 | -2.048534 | -1.014266 | 1.298221 | 0.401742 | -1.080608 | 0 | 1 |
| 291 | -0.572282 | -0.375532 | -2.067885 | -0.361247 | -0.315065 | -0.671820 | -0.183865 | -0.517694 | -0.802956 | -0.951809 | 0.282442 | 0.208005 | -0.271252 | 0 | 1 |
| 292 | -0.084382 | -1.508230 | -0.105496 | -1.930204 | -1.529664 | -0.795467 | 1.273717 | -1.858542 | -0.446361 | -0.239346 | 0.154464 | -0.114937 | -1.831603 | 0 | 1 |
| 293 | -1.172703 | 0.783209 | -1.141589 | -0.982768 | -0.513216 | 0.655437 | 1.962510 | 0.628858 | 1.130028 | 1.104741 | 1.539591 | 1.547843 | -0.011302 | 1 | 1 |
| 294 | -1.293038 | 0.838303 | -1.049071 | -0.708031 | -0.779995 | 0.868108 | 1.621994 | 0.725495 | 1.173585 | 1.424395 | 1.751950 | 1.352876 | 0.339922 | 1 | 1 |
| 295 | 0.809878 | -0.351504 | -2.231752 | -0.556719 | -1.430264 | -0.357918 | -0.727837 | 1.110363 | 1.684188 | 0.429768 | 0.560061 | 0.371789 | -1.110030 | 0 | 1 |
| 296 | 0.305449 | -0.148924 | -0.727054 | -0.126830 | 0.467272 | 0.420013 | 1.212777 | 0.954055 | -0.988419 | -0.423614 | -0.047239 | 0.058678 | -0.031517 | 0 | 1 |
| 297 | 1.212224 | 1.916789 | 0.287969 | -0.073842 | 0.289112 | 0.943764 | -0.395404 | -0.380613 | 0.262567 | 0.759137 | 0.277177 | 0.493951 | 1.026995 | 1 | 1 |
| 298 | -0.121307 | 0.217217 | 0.030920 | -0.201270 | -0.752001 | -0.276070 | 0.835502 | -0.363704 | -0.641199 | 0.283313 | 0.060013 | 0.013280 | 0.477857 | 1 | 1 |
| 299 | -0.622824 | -0.595352 | 0.256282 | -0.111551 | 0.023990 | 1.221659 | 1.572998 | -0.263983 | -0.707828 | 0.707801 | 0.306249 | 1.046476 | 0.214979 | 1 | 1 |
| 300 | -0.667480 | -0.808638 | 0.730781 | 0.054549 | 0.191421 | 0.279885 | 0.088177 | 0.823617 | 0.604299 | 0.640274 | -0.360151 | 1.298688 | 0.494875 | 1 | 1 |
| 301 | 0.928382 | -2.375767 | -0.427528 | -0.852350 | -1.137004 | 1.584181 | -1.700220 | -2.060965 | -1.326622 | 0.451948 | 0.593212 | 0.152418 | -0.128797 | 0 | 1 |
| 302 | -0.483888 | 0.443846 | 0.129714 | 0.199624 | -0.106985 | 0.817702 | -0.072817 | -1.163918 | 0.545762 | -0.141320 | 0.041767 | -0.402181 | 0.061897 | 1 | 1 |
| 303 | 0.715769 | 0.780533 | 1.467750 | -0.595580 | -1.178484 | 4.014345 | -0.112339 | -1.611382 | -0.295511 | 0.032462 | 1.836607 | -4.315898 | -1.084441 | 1 | 1 |
| 304 | 0.041466 | -0.470275 | 0.234655 | 0.109532 | -0.518455 | -0.977540 | -0.613498 | -1.108545 | 0.500653 | -0.214143 | -0.033265 | -0.541673 | 0.714974 | 1 | 1 |
| 305 | 0.818747 | 0.495675 | 1.005686 | 0.967334 | 0.505171 | -0.579478 | -0.847677 | 1.574323 | 1.544556 | 0.412556 | -0.972040 | 0.290457 | 0.289042 | 1 | 1 |
| 306 | 1.062928 | -1.149587 | 1.951840 | -0.065775 | 0.546680 | 0.994901 | -1.817826 | 2.109742 | 0.264443 | 0.505287 | -0.757462 | 0.578677 | 0.222503 | 1 | 1 |
| 307 | -0.701621 | -0.049803 | -0.719153 | -0.048069 | 1.223251 | 1.913492 | 0.887449 | 0.038186 | 0.546172 | -0.568362 | -1.091833 | -0.250367 | 0.831399 | 1 | 1 |
| 308 | -0.079821 | 0.796085 | -0.215763 | -1.396439 | -0.133350 | 0.582037 | 2.442796 | 0.743250 | -1.182753 | -0.723658 | -0.879934 | -2.498899 | -1.532262 | 0 | 1 |
309 rows × 15 columns
In [101]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[101]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82b505400>
In [222]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [268]:
X = df_n_ps_std_mfcc[3]
In [269]:
y = df_n_ps[3]['chosen']
In [270]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [271]:
X_train.shape
Out[271]:
(139, 13)
In [107]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [108]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [109]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [110]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.003, 'max_iter': 75}, que permiten obtener un Accuracy de 86.33% y un Kappa del 66.54
Tiempo total: 21.38 minutos
In [272]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30,20,10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.003
epochs = 75
In [273]:
input_tensor = Input(shape = (n0,))
In [274]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [275]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [276]:
model.summary()
Model: "model_12" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_12 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_37 (Dense) (None, 30) 420 _________________________________________________________________ dense_38 (Dense) (None, 20) 620 _________________________________________________________________ dense_39 (Dense) (None, 10) 210 _________________________________________________________________ dense_40 (Dense) (None, 1) 11 ================================================================= Total params: 1,261 Trainable params: 1,261 Non-trainable params: 0 _________________________________________________________________
In [277]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 139 samples, validate on 47 samples Epoch 1/75 139/139 [==============================] - 0s 2ms/step - loss: 0.7119 - accuracy: 0.4748 - val_loss: 0.6764 - val_accuracy: 0.5957 Epoch 2/75 139/139 [==============================] - 0s 79us/step - loss: 0.6629 - accuracy: 0.6619 - val_loss: 0.6286 - val_accuracy: 0.7021 Epoch 3/75 139/139 [==============================] - 0s 72us/step - loss: 0.6227 - accuracy: 0.7914 - val_loss: 0.5921 - val_accuracy: 0.7447 Epoch 4/75 139/139 [==============================] - 0s 79us/step - loss: 0.5865 - accuracy: 0.8058 - val_loss: 0.5597 - val_accuracy: 0.7660 Epoch 5/75 139/139 [==============================] - 0s 72us/step - loss: 0.5507 - accuracy: 0.8201 - val_loss: 0.5260 - val_accuracy: 0.7660 Epoch 6/75 139/139 [==============================] - 0s 79us/step - loss: 0.5136 - accuracy: 0.8489 - val_loss: 0.4960 - val_accuracy: 0.7872 Epoch 7/75 139/139 [==============================] - 0s 86us/step - loss: 0.4773 - accuracy: 0.8417 - val_loss: 0.4729 - val_accuracy: 0.7447 Epoch 8/75 139/139 [==============================] - 0s 72us/step - loss: 0.4456 - accuracy: 0.8417 - val_loss: 0.4574 - val_accuracy: 0.7872 Epoch 9/75 139/139 [==============================] - 0s 79us/step - loss: 0.4162 - accuracy: 0.8417 - val_loss: 0.4409 - val_accuracy: 0.8085 Epoch 10/75 139/139 [==============================] - 0s 93us/step - loss: 0.3904 - accuracy: 0.8561 - val_loss: 0.4262 - val_accuracy: 0.8085 Epoch 11/75 139/139 [==============================] - 0s 79us/step - loss: 0.3699 - accuracy: 0.8561 - val_loss: 0.4161 - val_accuracy: 0.8298 Epoch 12/75 139/139 [==============================] - 0s 79us/step - loss: 0.3578 - accuracy: 0.8633 - val_loss: 0.4064 - val_accuracy: 0.8298 Epoch 13/75 139/139 [==============================] - 0s 79us/step - loss: 0.3470 - accuracy: 0.8633 - val_loss: 0.4064 - val_accuracy: 0.8511 Epoch 14/75 139/139 [==============================] - 0s 79us/step - loss: 0.3383 - accuracy: 0.8633 - val_loss: 0.4040 - val_accuracy: 0.8298 Epoch 15/75 139/139 [==============================] - 0s 79us/step - loss: 0.3269 - accuracy: 0.8633 - val_loss: 0.4150 - val_accuracy: 0.8085 Epoch 16/75 139/139 [==============================] - 0s 72us/step - loss: 0.3161 - accuracy: 0.8777 - val_loss: 0.4421 - val_accuracy: 0.7660 Epoch 17/75 139/139 [==============================] - 0s 86us/step - loss: 0.3101 - accuracy: 0.8777 - val_loss: 0.4574 - val_accuracy: 0.7447 Epoch 18/75 139/139 [==============================] - 0s 79us/step - loss: 0.3062 - accuracy: 0.8993 - val_loss: 0.4546 - val_accuracy: 0.7872 Epoch 19/75 139/139 [==============================] - 0s 86us/step - loss: 0.2971 - accuracy: 0.8993 - val_loss: 0.4487 - val_accuracy: 0.7872 Epoch 20/75 139/139 [==============================] - 0s 79us/step - loss: 0.2904 - accuracy: 0.9065 - val_loss: 0.4424 - val_accuracy: 0.7872 Epoch 21/75 139/139 [==============================] - 0s 79us/step - loss: 0.2835 - accuracy: 0.9065 - val_loss: 0.4318 - val_accuracy: 0.7872 Epoch 22/75 139/139 [==============================] - 0s 115us/step - loss: 0.2776 - accuracy: 0.8921 - val_loss: 0.4251 - val_accuracy: 0.8085 Epoch 23/75 139/139 [==============================] - 0s 108us/step - loss: 0.2706 - accuracy: 0.9065 - val_loss: 0.4328 - val_accuracy: 0.8085 Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 24/75 139/139 [==============================] - 0s 144us/step - loss: 0.2633 - accuracy: 0.9137 - val_loss: 0.4410 - val_accuracy: 0.7872 Epoch 25/75 139/139 [==============================] - 0s 108us/step - loss: 0.2592 - accuracy: 0.9209 - val_loss: 0.4482 - val_accuracy: 0.7872 Epoch 26/75 139/139 [==============================] - 0s 86us/step - loss: 0.2557 - accuracy: 0.9281 - val_loss: 0.4575 - val_accuracy: 0.7872 Epoch 27/75 139/139 [==============================] - 0s 86us/step - loss: 0.2516 - accuracy: 0.9209 - val_loss: 0.4665 - val_accuracy: 0.7872 Epoch 28/75 139/139 [==============================] - 0s 115us/step - loss: 0.2478 - accuracy: 0.9137 - val_loss: 0.4634 - val_accuracy: 0.8085 Epoch 29/75 139/139 [==============================] - 0s 86us/step - loss: 0.2436 - accuracy: 0.9137 - val_loss: 0.4585 - val_accuracy: 0.8085 Epoch 30/75 139/139 [==============================] - 0s 93us/step - loss: 0.2404 - accuracy: 0.9137 - val_loss: 0.4543 - val_accuracy: 0.8298 Epoch 31/75 139/139 [==============================] - 0s 79us/step - loss: 0.2362 - accuracy: 0.9209 - val_loss: 0.4553 - val_accuracy: 0.8298 Epoch 32/75 139/139 [==============================] - 0s 86us/step - loss: 0.2339 - accuracy: 0.9137 - val_loss: 0.4600 - val_accuracy: 0.8298 Epoch 33/75 139/139 [==============================] - 0s 86us/step - loss: 0.2286 - accuracy: 0.9209 - val_loss: 0.4598 - val_accuracy: 0.8298 Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 34/75 139/139 [==============================] - 0s 86us/step - loss: 0.2252 - accuracy: 0.9209 - val_loss: 0.4618 - val_accuracy: 0.8298 Epoch 35/75 139/139 [==============================] - 0s 122us/step - loss: 0.2235 - accuracy: 0.9209 - val_loss: 0.4629 - val_accuracy: 0.8298 Epoch 36/75 139/139 [==============================] - 0s 101us/step - loss: 0.2216 - accuracy: 0.9209 - val_loss: 0.4644 - val_accuracy: 0.8298 Epoch 37/75 139/139 [==============================] - 0s 93us/step - loss: 0.2197 - accuracy: 0.9209 - val_loss: 0.4666 - val_accuracy: 0.8298 Epoch 38/75 139/139 [==============================] - 0s 93us/step - loss: 0.2176 - accuracy: 0.9209 - val_loss: 0.4677 - val_accuracy: 0.8298 Epoch 39/75 139/139 [==============================] - 0s 93us/step - loss: 0.2156 - accuracy: 0.9281 - val_loss: 0.4700 - val_accuracy: 0.8298 Epoch 40/75 139/139 [==============================] - 0s 86us/step - loss: 0.2140 - accuracy: 0.9281 - val_loss: 0.4730 - val_accuracy: 0.8298 Epoch 41/75 139/139 [==============================] - 0s 94us/step - loss: 0.2118 - accuracy: 0.9281 - val_loss: 0.4786 - val_accuracy: 0.8085 Epoch 42/75 139/139 [==============================] - 0s 101us/step - loss: 0.2098 - accuracy: 0.9281 - val_loss: 0.4820 - val_accuracy: 0.8085 Epoch 43/75 139/139 [==============================] - 0s 101us/step - loss: 0.2080 - accuracy: 0.9281 - val_loss: 0.4826 - val_accuracy: 0.8085 Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 44/75 139/139 [==============================] - 0s 93us/step - loss: 0.2061 - accuracy: 0.9281 - val_loss: 0.4818 - val_accuracy: 0.8085 Epoch 45/75 139/139 [==============================] - 0s 101us/step - loss: 0.2052 - accuracy: 0.9353 - val_loss: 0.4817 - val_accuracy: 0.8085 Epoch 46/75 139/139 [==============================] - 0s 101us/step - loss: 0.2044 - accuracy: 0.9353 - val_loss: 0.4812 - val_accuracy: 0.8085 Epoch 47/75 139/139 [==============================] - 0s 86us/step - loss: 0.2033 - accuracy: 0.9353 - val_loss: 0.4803 - val_accuracy: 0.8085 Epoch 48/75 139/139 [==============================] - 0s 86us/step - loss: 0.2023 - accuracy: 0.9353 - val_loss: 0.4808 - val_accuracy: 0.8085 Epoch 49/75 139/139 [==============================] - 0s 93us/step - loss: 0.2014 - accuracy: 0.9281 - val_loss: 0.4804 - val_accuracy: 0.8298 Epoch 50/75 139/139 [==============================] - 0s 79us/step - loss: 0.2006 - accuracy: 0.9281 - val_loss: 0.4809 - val_accuracy: 0.8298 Epoch 51/75 139/139 [==============================] - 0s 86us/step - loss: 0.1999 - accuracy: 0.9281 - val_loss: 0.4809 - val_accuracy: 0.8298 Epoch 52/75 139/139 [==============================] - 0s 86us/step - loss: 0.1990 - accuracy: 0.9281 - val_loss: 0.4836 - val_accuracy: 0.8085 Epoch 53/75 139/139 [==============================] - 0s 101us/step - loss: 0.1977 - accuracy: 0.9281 - val_loss: 0.4847 - val_accuracy: 0.8085 Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 54/75 139/139 [==============================] - 0s 101us/step - loss: 0.1969 - accuracy: 0.9281 - val_loss: 0.4852 - val_accuracy: 0.8085 Epoch 55/75 139/139 [==============================] - 0s 86us/step - loss: 0.1964 - accuracy: 0.9281 - val_loss: 0.4860 - val_accuracy: 0.8085 Epoch 56/75 139/139 [==============================] - 0s 93us/step - loss: 0.1960 - accuracy: 0.9281 - val_loss: 0.4854 - val_accuracy: 0.8085 Epoch 57/75 139/139 [==============================] - 0s 93us/step - loss: 0.1955 - accuracy: 0.9281 - val_loss: 0.4850 - val_accuracy: 0.8085 Epoch 58/75 139/139 [==============================] - 0s 101us/step - loss: 0.1950 - accuracy: 0.9281 - val_loss: 0.4849 - val_accuracy: 0.8298 Epoch 59/75 139/139 [==============================] - 0s 79us/step - loss: 0.1946 - accuracy: 0.9281 - val_loss: 0.4848 - val_accuracy: 0.8298 Epoch 60/75 139/139 [==============================] - 0s 93us/step - loss: 0.1940 - accuracy: 0.9281 - val_loss: 0.4851 - val_accuracy: 0.8298 Epoch 61/75 139/139 [==============================] - 0s 86us/step - loss: 0.1936 - accuracy: 0.9281 - val_loss: 0.4849 - val_accuracy: 0.8298 Epoch 62/75 139/139 [==============================] - 0s 86us/step - loss: 0.1932 - accuracy: 0.9353 - val_loss: 0.4862 - val_accuracy: 0.8085 Epoch 63/75 139/139 [==============================] - 0s 86us/step - loss: 0.1927 - accuracy: 0.9353 - val_loss: 0.4871 - val_accuracy: 0.8085 Epoch 00063: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 64/75 139/139 [==============================] - 0s 137us/step - loss: 0.1922 - accuracy: 0.9353 - val_loss: 0.4875 - val_accuracy: 0.8085 Epoch 65/75 139/139 [==============================] - 0s 93us/step - loss: 0.1919 - accuracy: 0.9353 - val_loss: 0.4880 - val_accuracy: 0.8085 Epoch 66/75 139/139 [==============================] - 0s 93us/step - loss: 0.1917 - accuracy: 0.9353 - val_loss: 0.4891 - val_accuracy: 0.8085 Epoch 67/75 139/139 [==============================] - 0s 93us/step - loss: 0.1914 - accuracy: 0.9424 - val_loss: 0.4895 - val_accuracy: 0.8085 Epoch 68/75 139/139 [==============================] - 0s 129us/step - loss: 0.1912 - accuracy: 0.9424 - val_loss: 0.4900 - val_accuracy: 0.8085 Epoch 69/75 139/139 [==============================] - 0s 122us/step - loss: 0.1909 - accuracy: 0.9424 - val_loss: 0.4904 - val_accuracy: 0.8085 Epoch 70/75 139/139 [==============================] - 0s 129us/step - loss: 0.1908 - accuracy: 0.9424 - val_loss: 0.4912 - val_accuracy: 0.8085 Epoch 71/75 139/139 [==============================] - 0s 101us/step - loss: 0.1905 - accuracy: 0.9424 - val_loss: 0.4911 - val_accuracy: 0.8085 Epoch 72/75 139/139 [==============================] - 0s 86us/step - loss: 0.1902 - accuracy: 0.9424 - val_loss: 0.4912 - val_accuracy: 0.8085 Epoch 73/75 139/139 [==============================] - 0s 79us/step - loss: 0.1900 - accuracy: 0.9424 - val_loss: 0.4910 - val_accuracy: 0.8085 Epoch 00073: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 74/75 139/139 [==============================] - 0s 86us/step - loss: 0.1898 - accuracy: 0.9424 - val_loss: 0.4910 - val_accuracy: 0.8085 Epoch 75/75 139/139 [==============================] - 0s 72us/step - loss: 0.1896 - accuracy: 0.9424 - val_loss: 0.4910 - val_accuracy: 0.8085
In [278]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 75)
In [279]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
47/47 [==============================] - 0s 170us/step test loss: 0.49097854279457254, test accuracy: 0.8085106611251831
In [280]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.8571428571428572
In [281]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.5346534653465347
KMeans¶
In [121]:
X
Out[121]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.311006 | 1.696486 | 0.912001 | -0.211934 | -0.513557 | 1.357699 | 0.777385 | 0.508585 | -2.290902 | -2.422249 | -0.738438 | -2.221933 | -1.191363 |
| 1 | 0.947147 | -0.576741 | -1.258913 | -0.786859 | 0.887961 | -1.895175 | -0.310427 | -0.374360 | 1.478062 | 0.972075 | -1.105984 | 0.612318 | -1.486887 |
| 2 | -0.115048 | 1.257050 | 0.043002 | -2.677464 | 3.902183 | -1.091787 | 0.505797 | 2.341684 | -2.345224 | -1.678088 | -2.608854 | -2.617777 | -2.135652 |
| 3 | 0.621032 | 1.417449 | 1.399722 | -0.625673 | 1.012110 | 0.230671 | -0.287988 | 1.012771 | -2.250326 | -0.340971 | -0.353905 | -0.717440 | -0.390485 |
| 4 | 0.340978 | 1.662814 | -1.775422 | 0.156552 | 1.678811 | 0.301711 | 2.038462 | 1.511985 | 1.508787 | -2.046602 | 0.741073 | -0.282747 | -0.779814 |
| 5 | 0.426765 | -1.056701 | -1.244088 | -0.696846 | -0.372415 | -0.847420 | -0.209607 | 1.462924 | -0.541420 | 0.000628 | -1.135148 | 1.608546 | 1.709532 |
| 6 | 0.210857 | -1.779497 | -2.206121 | -0.832640 | 0.636169 | -1.979858 | -0.510102 | 1.437770 | 0.128209 | 0.025521 | 0.184211 | 2.300204 | 0.912793 |
| 7 | -0.821293 | -0.049796 | 0.237440 | 0.379918 | 0.714133 | 0.670070 | 0.122605 | -0.069298 | -0.126759 | -0.199559 | 0.547891 | -0.099623 | -0.024895 |
| 8 | 0.420103 | -0.662020 | -0.550543 | -0.566406 | -0.923203 | -0.295152 | -0.533234 | 0.927026 | 0.119135 | 0.218761 | -0.245778 | 0.627242 | 1.313952 |
| 9 | -1.436247 | 0.435343 | 2.482690 | 1.099668 | -0.392845 | 0.565039 | 0.569531 | -0.088218 | -0.131137 | -0.699769 | -0.538549 | -0.329443 | 0.942919 |
| 10 | -1.574051 | -1.334372 | -1.636184 | 1.768991 | -0.369456 | -0.008046 | -1.402331 | 0.012625 | 1.135935 | 1.623145 | -0.653935 | 0.182348 | 1.052310 |
| 11 | -1.798986 | -1.632467 | -1.314854 | 2.656006 | -0.096678 | -0.174852 | -1.748372 | 0.185804 | 0.930317 | 0.365776 | -0.676448 | 0.358271 | 1.523770 |
| 12 | -0.708207 | 0.931180 | 0.258840 | -0.189291 | -0.204832 | -0.103872 | 0.221697 | -0.231695 | -0.003439 | 0.423528 | 1.259835 | 0.119625 | -0.192417 |
| 13 | -2.007033 | -0.288096 | 0.099713 | 0.390909 | 1.333138 | -0.069950 | 0.643074 | 0.172080 | -0.109666 | 0.304475 | -1.157528 | -1.708326 | -1.420079 |
| 14 | -0.497985 | 0.020592 | -0.123619 | 0.165046 | -0.765078 | -0.465219 | 0.172533 | 0.722853 | 0.284863 | -0.035284 | 0.024769 | -0.065990 | -0.992437 |
| 15 | 1.200625 | 0.984580 | -0.234312 | 0.348855 | 0.175663 | 0.309396 | 0.390611 | -0.745912 | -0.667554 | -0.052439 | 0.119610 | -0.862930 | 0.945979 |
| 16 | 0.435253 | 3.280178 | 0.407736 | 1.143148 | 2.291571 | 0.546530 | 0.170667 | 0.427708 | -0.063936 | -0.532360 | 0.404150 | 0.415849 | 0.869331 |
| 17 | -0.398944 | 0.035026 | -1.634042 | -1.354378 | 0.854385 | 1.406182 | -0.773335 | 0.663902 | 0.928496 | 1.278830 | 0.464511 | 0.235475 | -0.040374 |
| 18 | -0.454008 | -0.234096 | -0.930672 | -0.507506 | 0.545773 | 0.437756 | 1.026910 | 0.013959 | -0.620099 | -0.593763 | 1.073690 | 0.594340 | 0.987056 |
| 19 | 0.149846 | 0.062252 | -0.002122 | 0.786346 | 0.810930 | 0.304880 | -0.882886 | -0.043156 | 2.503584 | 0.894947 | 0.394981 | 0.761651 | 0.402963 |
| 20 | -0.314274 | 0.446482 | 0.889744 | 0.891114 | 1.249237 | 0.718469 | 0.296834 | -0.831548 | -0.393364 | -0.103574 | 0.295790 | 0.092061 | 0.424633 |
| 21 | 0.659365 | 1.053258 | -0.877939 | -0.295954 | -1.122110 | -0.035202 | 1.512616 | 0.031457 | -0.700740 | -1.687204 | -1.136215 | -1.545451 | -0.082548 |
| 22 | 0.568507 | -0.357318 | -1.183577 | -0.069205 | 0.462644 | -0.956011 | 0.501504 | 0.240708 | -0.025482 | 0.416003 | 0.237690 | -0.566935 | -0.846151 |
| 23 | 0.696474 | 0.477607 | -1.637469 | -1.158983 | -2.224208 | -1.861929 | -0.176558 | 0.694585 | 0.426826 | -0.088376 | -0.335290 | 1.125320 | 0.705700 |
| 24 | -0.221795 | -0.513464 | -0.506448 | 0.594506 | 0.033232 | -1.141879 | -1.582503 | -0.081204 | -0.001962 | -0.704687 | -0.473528 | 0.580117 | 1.533686 |
| 25 | 0.036099 | -0.007586 | 0.116729 | 0.438081 | -1.526141 | -1.994283 | -1.014100 | 0.028630 | -0.553238 | -0.540795 | 0.467730 | 0.943285 | 0.498193 |
| 26 | -0.291576 | -0.372192 | -1.176599 | 0.078535 | 0.516288 | -1.851892 | -2.218803 | 0.335200 | 0.323222 | 0.006649 | 0.017717 | 0.133172 | 1.208725 |
| 27 | 0.953536 | 0.427304 | -0.554063 | 0.425439 | 1.368674 | 0.362392 | 0.477030 | -0.976616 | -0.382390 | 0.310619 | -0.903078 | -0.943886 | -0.047616 |
| 28 | -1.172014 | 1.307258 | -1.059323 | -0.655908 | 1.591107 | 0.483432 | 0.474862 | 0.348014 | -0.527448 | 0.798802 | -0.075253 | 1.943808 | 0.108268 |
| 29 | -0.954427 | 0.000731 | -0.367958 | 0.281024 | 0.303337 | 0.744504 | 1.271647 | 0.298340 | -0.057042 | -0.297712 | -0.053703 | -0.045043 | -0.561554 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 156 | 0.232363 | -1.167339 | -0.114632 | 1.240724 | -0.209611 | 0.597503 | -0.105216 | -0.393018 | -0.168804 | -0.038601 | 0.602075 | -0.482733 | 0.192333 |
| 157 | -1.686193 | -0.806140 | -0.531342 | -0.411912 | 0.312945 | 0.751058 | 0.624837 | -0.394463 | 0.549120 | -1.174079 | -1.374572 | -1.950144 | -0.652535 |
| 158 | 0.487798 | 1.116042 | -0.308817 | 0.175231 | -0.191701 | -0.682970 | 0.502123 | 0.749073 | 1.365476 | 0.198244 | 1.283992 | 0.132188 | 0.482532 |
| 159 | 1.049575 | 0.742765 | 0.000505 | 0.670386 | 0.235663 | -0.297404 | 0.891743 | 0.047729 | 0.086633 | 0.873400 | 0.552393 | 0.496793 | 0.659122 |
| 160 | 0.285967 | 0.602916 | -0.009050 | 0.802464 | 0.333031 | -1.182611 | 0.473870 | 0.896236 | 0.890391 | 0.208214 | 0.786475 | 0.044481 | -0.114927 |
| 161 | 2.568510 | -0.180837 | 0.794882 | 1.410838 | 0.898076 | 0.468184 | 0.963255 | 0.338074 | 2.081580 | 2.353196 | 0.146660 | -0.295606 | -0.020484 |
| 162 | 0.821849 | 0.906757 | 0.282262 | 0.304716 | -0.691824 | 0.772704 | 2.543328 | -0.404440 | 1.861464 | 1.635426 | 0.204673 | 0.084333 | 0.469447 |
| 163 | 2.581037 | 0.239015 | 1.212048 | 0.498566 | 0.095720 | 0.062469 | 3.463238 | 0.374969 | -0.054235 | -0.365031 | -0.169020 | 1.160964 | 0.666076 |
| 164 | -0.247271 | -0.874145 | -0.840584 | 0.233138 | 0.034101 | 0.259892 | 0.144353 | -0.570094 | 1.244117 | 0.282845 | 0.127444 | -0.721587 | -1.450860 |
| 165 | 0.188979 | -0.519200 | 0.108496 | -0.513645 | -0.637646 | 0.812515 | 0.626360 | -0.156977 | -0.092241 | -0.517923 | 0.026563 | -0.597616 | -0.101096 |
| 166 | -0.015438 | -0.656621 | -0.739614 | 0.302131 | 0.583862 | 0.465267 | 0.342075 | -0.318902 | 0.221544 | 0.654368 | 0.777463 | -0.462212 | -0.867288 |
| 167 | -1.567081 | -1.052883 | -0.417918 | 0.636963 | -0.531279 | 0.787238 | -1.913461 | -0.020653 | -0.111129 | 0.112259 | -0.380422 | 0.497894 | 0.709826 |
| 168 | -1.883530 | -0.172892 | -0.340073 | -0.255266 | -0.480237 | -0.061425 | -0.158589 | -0.308725 | -0.034923 | 0.150845 | 0.696367 | 0.704196 | 0.473391 |
| 169 | -1.577057 | -0.602693 | 0.448785 | 1.073850 | -0.714538 | 1.427240 | -1.645225 | 0.812069 | -0.019466 | -0.719024 | -0.991241 | 0.521497 | 0.461555 |
| 170 | 0.667824 | -0.298287 | -0.412356 | -1.154598 | 0.171532 | -0.341146 | -0.411827 | -1.296671 | 0.428160 | -0.233124 | 0.035111 | -0.934501 | 0.123701 |
| 171 | 0.626343 | -0.046406 | -0.168999 | -1.278941 | 0.502027 | -0.060296 | 0.062571 | -1.284727 | 0.028380 | -0.488598 | -0.255008 | -1.199407 | 0.088115 |
| 172 | -0.441233 | -0.987406 | 0.015664 | -2.982951 | -0.379800 | 1.485748 | -2.174788 | -3.521120 | -0.191502 | 2.486906 | 2.771782 | 0.789523 | 0.553119 |
| 173 | 0.331514 | 0.334707 | -0.187508 | 0.489055 | 1.133140 | 1.016598 | -0.572753 | -0.634721 | -0.567790 | -0.492410 | 0.634219 | -0.122575 | -0.205540 |
| 174 | 0.480131 | 0.345645 | 0.202709 | -0.423456 | 1.184414 | 2.116965 | -0.463050 | 0.212550 | 1.973473 | -0.996794 | 0.924229 | -0.170049 | -0.068464 |
| 175 | 1.039410 | -0.773764 | 0.113739 | -0.796036 | -1.053802 | -1.238009 | 0.153897 | 0.497600 | 1.347261 | 0.972165 | 0.993095 | 0.066125 | -0.069772 |
| 176 | 0.194148 | -0.229033 | -0.571129 | -0.704359 | -0.204400 | -0.273049 | 1.105329 | 0.052851 | -0.360196 | 0.099095 | 1.063628 | -0.266594 | 0.041526 |
| 177 | -0.169188 | 0.025195 | -0.189648 | 0.376353 | 0.802036 | -1.185140 | 0.488985 | 0.244963 | 1.305631 | 0.241661 | 0.402415 | -0.494815 | 0.252206 |
| 178 | 1.433007 | 0.217051 | -0.388425 | -1.158798 | 2.068592 | 1.278810 | -1.193547 | -0.909321 | -0.207122 | 2.062093 | 1.374797 | 0.383804 | 1.569650 |
| 179 | 1.371536 | 0.624596 | -0.082552 | 0.444824 | 1.959112 | -0.736647 | -1.448177 | 0.624897 | 1.304939 | -0.025270 | -0.519401 | 0.592135 | 1.312240 |
| 180 | 0.815061 | -1.210119 | 0.844643 | -1.152602 | -0.216878 | -1.573232 | -0.065062 | 2.136014 | -0.285964 | 1.827988 | -0.982121 | 1.139199 | 0.936226 |
| 181 | 0.814962 | -1.028970 | -1.340094 | -1.579784 | 0.774822 | -0.351654 | -2.148181 | 2.772395 | 1.638263 | -0.394371 | 1.796246 | 1.182459 | 0.824064 |
| 182 | 1.615277 | 0.706391 | -0.611277 | 0.513438 | 0.987249 | 1.226124 | 0.240966 | 0.485917 | 1.355615 | -0.480955 | -0.255325 | -0.370864 | 0.107591 |
| 183 | 0.290224 | 0.578762 | 0.024629 | 0.119894 | 0.626180 | 1.025427 | 0.180541 | -0.504388 | -1.085411 | -1.413825 | 0.811722 | 0.640653 | 0.433677 |
| 184 | 0.086408 | -1.394139 | -0.501233 | 1.251905 | -0.481983 | 0.026482 | -1.317983 | -0.580623 | -0.160381 | -0.718194 | 0.110108 | -0.183905 | 0.074891 |
| 185 | 0.024909 | -0.713904 | -1.235134 | -0.194562 | 0.155358 | -0.586587 | -0.455970 | 0.577457 | 1.172268 | 0.468799 | 0.500130 | 1.133624 | 0.192845 |
186 rows × 13 columns
In [122]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[122]:
[2418.0, 2172.1027618675616, 1998.025251732407, 1881.5714074571665, 1790.4988909951144, 1710.5509624175916, 1638.1160961205187, 1594.2979605381602, 1541.5219098889218, 1492.348355544395, 1435.6626781919845, 1397.526425851007, 1339.3046499586067, 1333.0833102955885]
In [123]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[123]:
[<matplotlib.lines.Line2D at 0x1e82cc61860>]
K=3
In [124]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[124]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [125]:
kmeans_mfcc.labels_
Out[125]:
array([1, 2, 1, 1, 0, 2, 2, 0, 2, 0, 2, 2, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1,
1, 2, 2, 2, 2, 0, 0, 1, 2, 2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 1, 1, 1,
1, 2, 0, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 1, 2,
2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 0, 2, 1, 0, 2, 1, 0, 1, 2, 2,
0, 1, 1, 0, 1, 0, 2, 2, 2, 1, 2, 2, 0, 1, 2, 1, 2, 2, 2, 1, 1, 1,
2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 1, 1, 0, 2, 1, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 1, 2, 2, 1, 0, 2, 2, 1, 0, 1, 0,
1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 2, 0, 0, 2,
1, 0, 0, 0, 2, 2, 0, 0, 2, 2])
In [126]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[126]:
array([1, 2, 1, 1, 0, 2, 2, 0, 2, 0, 2, 2, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1,
1, 2, 2, 2, 2, 0, 0, 1, 2, 2, 2, 1, 1, 0, 2, 2, 2, 0, 2, 1, 1, 1,
1, 2, 0, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 1, 2,
2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 0, 2, 1, 0, 2, 1, 0, 1, 2, 2,
0, 1, 1, 0, 1, 0, 2, 2, 2, 1, 2, 2, 0, 1, 2, 1, 2, 2, 2, 1, 1, 1,
2, 2, 2, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 1, 1, 0, 2, 1, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 1, 2, 2, 1, 0, 2, 2, 1, 0, 1, 0,
1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 2, 0, 0, 2,
1, 0, 0, 0, 2, 2, 0, 0, 2, 2])
In [127]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [128]:
X
Out[128]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.311006 | 1.696486 | 0.912001 | -0.211934 | -0.513557 | 1.357699 | 0.777385 | 0.508585 | -2.290902 | -2.422249 | -0.738438 | -2.221933 | -1.191363 | 1 | 0 |
| 1 | 0.947147 | -0.576741 | -1.258913 | -0.786859 | 0.887961 | -1.895175 | -0.310427 | -0.374360 | 1.478062 | 0.972075 | -1.105984 | 0.612318 | -1.486887 | 2 | 0 |
| 2 | -0.115048 | 1.257050 | 0.043002 | -2.677464 | 3.902183 | -1.091787 | 0.505797 | 2.341684 | -2.345224 | -1.678088 | -2.608854 | -2.617777 | -2.135652 | 1 | 0 |
| 3 | 0.621032 | 1.417449 | 1.399722 | -0.625673 | 1.012110 | 0.230671 | -0.287988 | 1.012771 | -2.250326 | -0.340971 | -0.353905 | -0.717440 | -0.390485 | 1 | 0 |
| 4 | 0.340978 | 1.662814 | -1.775422 | 0.156552 | 1.678811 | 0.301711 | 2.038462 | 1.511985 | 1.508787 | -2.046602 | 0.741073 | -0.282747 | -0.779814 | 0 | 0 |
| 5 | 0.426765 | -1.056701 | -1.244088 | -0.696846 | -0.372415 | -0.847420 | -0.209607 | 1.462924 | -0.541420 | 0.000628 | -1.135148 | 1.608546 | 1.709532 | 2 | 0 |
| 6 | 0.210857 | -1.779497 | -2.206121 | -0.832640 | 0.636169 | -1.979858 | -0.510102 | 1.437770 | 0.128209 | 0.025521 | 0.184211 | 2.300204 | 0.912793 | 2 | 0 |
| 7 | -0.821293 | -0.049796 | 0.237440 | 0.379918 | 0.714133 | 0.670070 | 0.122605 | -0.069298 | -0.126759 | -0.199559 | 0.547891 | -0.099623 | -0.024895 | 0 | 0 |
| 8 | 0.420103 | -0.662020 | -0.550543 | -0.566406 | -0.923203 | -0.295152 | -0.533234 | 0.927026 | 0.119135 | 0.218761 | -0.245778 | 0.627242 | 1.313952 | 2 | 0 |
| 9 | -1.436247 | 0.435343 | 2.482690 | 1.099668 | -0.392845 | 0.565039 | 0.569531 | -0.088218 | -0.131137 | -0.699769 | -0.538549 | -0.329443 | 0.942919 | 0 | 0 |
| 10 | -1.574051 | -1.334372 | -1.636184 | 1.768991 | -0.369456 | -0.008046 | -1.402331 | 0.012625 | 1.135935 | 1.623145 | -0.653935 | 0.182348 | 1.052310 | 2 | 0 |
| 11 | -1.798986 | -1.632467 | -1.314854 | 2.656006 | -0.096678 | -0.174852 | -1.748372 | 0.185804 | 0.930317 | 0.365776 | -0.676448 | 0.358271 | 1.523770 | 2 | 0 |
| 12 | -0.708207 | 0.931180 | 0.258840 | -0.189291 | -0.204832 | -0.103872 | 0.221697 | -0.231695 | -0.003439 | 0.423528 | 1.259835 | 0.119625 | -0.192417 | 0 | 0 |
| 13 | -2.007033 | -0.288096 | 0.099713 | 0.390909 | 1.333138 | -0.069950 | 0.643074 | 0.172080 | -0.109666 | 0.304475 | -1.157528 | -1.708326 | -1.420079 | 1 | 0 |
| 14 | -0.497985 | 0.020592 | -0.123619 | 0.165046 | -0.765078 | -0.465219 | 0.172533 | 0.722853 | 0.284863 | -0.035284 | 0.024769 | -0.065990 | -0.992437 | 1 | 0 |
| 15 | 1.200625 | 0.984580 | -0.234312 | 0.348855 | 0.175663 | 0.309396 | 0.390611 | -0.745912 | -0.667554 | -0.052439 | 0.119610 | -0.862930 | 0.945979 | 0 | 0 |
| 16 | 0.435253 | 3.280178 | 0.407736 | 1.143148 | 2.291571 | 0.546530 | 0.170667 | 0.427708 | -0.063936 | -0.532360 | 0.404150 | 0.415849 | 0.869331 | 0 | 0 |
| 17 | -0.398944 | 0.035026 | -1.634042 | -1.354378 | 0.854385 | 1.406182 | -0.773335 | 0.663902 | 0.928496 | 1.278830 | 0.464511 | 0.235475 | -0.040374 | 2 | 0 |
| 18 | -0.454008 | -0.234096 | -0.930672 | -0.507506 | 0.545773 | 0.437756 | 1.026910 | 0.013959 | -0.620099 | -0.593763 | 1.073690 | 0.594340 | 0.987056 | 0 | 0 |
| 19 | 0.149846 | 0.062252 | -0.002122 | 0.786346 | 0.810930 | 0.304880 | -0.882886 | -0.043156 | 2.503584 | 0.894947 | 0.394981 | 0.761651 | 0.402963 | 0 | 0 |
| 20 | -0.314274 | 0.446482 | 0.889744 | 0.891114 | 1.249237 | 0.718469 | 0.296834 | -0.831548 | -0.393364 | -0.103574 | 0.295790 | 0.092061 | 0.424633 | 0 | 0 |
| 21 | 0.659365 | 1.053258 | -0.877939 | -0.295954 | -1.122110 | -0.035202 | 1.512616 | 0.031457 | -0.700740 | -1.687204 | -1.136215 | -1.545451 | -0.082548 | 1 | 0 |
| 22 | 0.568507 | -0.357318 | -1.183577 | -0.069205 | 0.462644 | -0.956011 | 0.501504 | 0.240708 | -0.025482 | 0.416003 | 0.237690 | -0.566935 | -0.846151 | 1 | 0 |
| 23 | 0.696474 | 0.477607 | -1.637469 | -1.158983 | -2.224208 | -1.861929 | -0.176558 | 0.694585 | 0.426826 | -0.088376 | -0.335290 | 1.125320 | 0.705700 | 2 | 0 |
| 24 | -0.221795 | -0.513464 | -0.506448 | 0.594506 | 0.033232 | -1.141879 | -1.582503 | -0.081204 | -0.001962 | -0.704687 | -0.473528 | 0.580117 | 1.533686 | 2 | 0 |
| 25 | 0.036099 | -0.007586 | 0.116729 | 0.438081 | -1.526141 | -1.994283 | -1.014100 | 0.028630 | -0.553238 | -0.540795 | 0.467730 | 0.943285 | 0.498193 | 2 | 0 |
| 26 | -0.291576 | -0.372192 | -1.176599 | 0.078535 | 0.516288 | -1.851892 | -2.218803 | 0.335200 | 0.323222 | 0.006649 | 0.017717 | 0.133172 | 1.208725 | 2 | 0 |
| 27 | 0.953536 | 0.427304 | -0.554063 | 0.425439 | 1.368674 | 0.362392 | 0.477030 | -0.976616 | -0.382390 | 0.310619 | -0.903078 | -0.943886 | -0.047616 | 0 | 0 |
| 28 | -1.172014 | 1.307258 | -1.059323 | -0.655908 | 1.591107 | 0.483432 | 0.474862 | 0.348014 | -0.527448 | 0.798802 | -0.075253 | 1.943808 | 0.108268 | 0 | 0 |
| 29 | -0.954427 | 0.000731 | -0.367958 | 0.281024 | 0.303337 | 0.744504 | 1.271647 | 0.298340 | -0.057042 | -0.297712 | -0.053703 | -0.045043 | -0.561554 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 156 | 0.232363 | -1.167339 | -0.114632 | 1.240724 | -0.209611 | 0.597503 | -0.105216 | -0.393018 | -0.168804 | -0.038601 | 0.602075 | -0.482733 | 0.192333 | 0 | 1 |
| 157 | -1.686193 | -0.806140 | -0.531342 | -0.411912 | 0.312945 | 0.751058 | 0.624837 | -0.394463 | 0.549120 | -1.174079 | -1.374572 | -1.950144 | -0.652535 | 1 | 1 |
| 158 | 0.487798 | 1.116042 | -0.308817 | 0.175231 | -0.191701 | -0.682970 | 0.502123 | 0.749073 | 1.365476 | 0.198244 | 1.283992 | 0.132188 | 0.482532 | 0 | 1 |
| 159 | 1.049575 | 0.742765 | 0.000505 | 0.670386 | 0.235663 | -0.297404 | 0.891743 | 0.047729 | 0.086633 | 0.873400 | 0.552393 | 0.496793 | 0.659122 | 0 | 1 |
| 160 | 0.285967 | 0.602916 | -0.009050 | 0.802464 | 0.333031 | -1.182611 | 0.473870 | 0.896236 | 0.890391 | 0.208214 | 0.786475 | 0.044481 | -0.114927 | 0 | 1 |
| 161 | 2.568510 | -0.180837 | 0.794882 | 1.410838 | 0.898076 | 0.468184 | 0.963255 | 0.338074 | 2.081580 | 2.353196 | 0.146660 | -0.295606 | -0.020484 | 0 | 1 |
| 162 | 0.821849 | 0.906757 | 0.282262 | 0.304716 | -0.691824 | 0.772704 | 2.543328 | -0.404440 | 1.861464 | 1.635426 | 0.204673 | 0.084333 | 0.469447 | 0 | 1 |
| 163 | 2.581037 | 0.239015 | 1.212048 | 0.498566 | 0.095720 | 0.062469 | 3.463238 | 0.374969 | -0.054235 | -0.365031 | -0.169020 | 1.160964 | 0.666076 | 0 | 1 |
| 164 | -0.247271 | -0.874145 | -0.840584 | 0.233138 | 0.034101 | 0.259892 | 0.144353 | -0.570094 | 1.244117 | 0.282845 | 0.127444 | -0.721587 | -1.450860 | 1 | 1 |
| 165 | 0.188979 | -0.519200 | 0.108496 | -0.513645 | -0.637646 | 0.812515 | 0.626360 | -0.156977 | -0.092241 | -0.517923 | 0.026563 | -0.597616 | -0.101096 | 1 | 1 |
| 166 | -0.015438 | -0.656621 | -0.739614 | 0.302131 | 0.583862 | 0.465267 | 0.342075 | -0.318902 | 0.221544 | 0.654368 | 0.777463 | -0.462212 | -0.867288 | 1 | 1 |
| 167 | -1.567081 | -1.052883 | -0.417918 | 0.636963 | -0.531279 | 0.787238 | -1.913461 | -0.020653 | -0.111129 | 0.112259 | -0.380422 | 0.497894 | 0.709826 | 2 | 1 |
| 168 | -1.883530 | -0.172892 | -0.340073 | -0.255266 | -0.480237 | -0.061425 | -0.158589 | -0.308725 | -0.034923 | 0.150845 | 0.696367 | 0.704196 | 0.473391 | 2 | 1 |
| 169 | -1.577057 | -0.602693 | 0.448785 | 1.073850 | -0.714538 | 1.427240 | -1.645225 | 0.812069 | -0.019466 | -0.719024 | -0.991241 | 0.521497 | 0.461555 | 2 | 1 |
| 170 | 0.667824 | -0.298287 | -0.412356 | -1.154598 | 0.171532 | -0.341146 | -0.411827 | -1.296671 | 0.428160 | -0.233124 | 0.035111 | -0.934501 | 0.123701 | 1 | 1 |
| 171 | 0.626343 | -0.046406 | -0.168999 | -1.278941 | 0.502027 | -0.060296 | 0.062571 | -1.284727 | 0.028380 | -0.488598 | -0.255008 | -1.199407 | 0.088115 | 1 | 1 |
| 172 | -0.441233 | -0.987406 | 0.015664 | -2.982951 | -0.379800 | 1.485748 | -2.174788 | -3.521120 | -0.191502 | 2.486906 | 2.771782 | 0.789523 | 0.553119 | 2 | 1 |
| 173 | 0.331514 | 0.334707 | -0.187508 | 0.489055 | 1.133140 | 1.016598 | -0.572753 | -0.634721 | -0.567790 | -0.492410 | 0.634219 | -0.122575 | -0.205540 | 0 | 1 |
| 174 | 0.480131 | 0.345645 | 0.202709 | -0.423456 | 1.184414 | 2.116965 | -0.463050 | 0.212550 | 1.973473 | -0.996794 | 0.924229 | -0.170049 | -0.068464 | 0 | 1 |
| 175 | 1.039410 | -0.773764 | 0.113739 | -0.796036 | -1.053802 | -1.238009 | 0.153897 | 0.497600 | 1.347261 | 0.972165 | 0.993095 | 0.066125 | -0.069772 | 2 | 1 |
| 176 | 0.194148 | -0.229033 | -0.571129 | -0.704359 | -0.204400 | -0.273049 | 1.105329 | 0.052851 | -0.360196 | 0.099095 | 1.063628 | -0.266594 | 0.041526 | 1 | 1 |
| 177 | -0.169188 | 0.025195 | -0.189648 | 0.376353 | 0.802036 | -1.185140 | 0.488985 | 0.244963 | 1.305631 | 0.241661 | 0.402415 | -0.494815 | 0.252206 | 0 | 1 |
| 178 | 1.433007 | 0.217051 | -0.388425 | -1.158798 | 2.068592 | 1.278810 | -1.193547 | -0.909321 | -0.207122 | 2.062093 | 1.374797 | 0.383804 | 1.569650 | 0 | 1 |
| 179 | 1.371536 | 0.624596 | -0.082552 | 0.444824 | 1.959112 | -0.736647 | -1.448177 | 0.624897 | 1.304939 | -0.025270 | -0.519401 | 0.592135 | 1.312240 | 0 | 1 |
| 180 | 0.815061 | -1.210119 | 0.844643 | -1.152602 | -0.216878 | -1.573232 | -0.065062 | 2.136014 | -0.285964 | 1.827988 | -0.982121 | 1.139199 | 0.936226 | 2 | 1 |
| 181 | 0.814962 | -1.028970 | -1.340094 | -1.579784 | 0.774822 | -0.351654 | -2.148181 | 2.772395 | 1.638263 | -0.394371 | 1.796246 | 1.182459 | 0.824064 | 2 | 1 |
| 182 | 1.615277 | 0.706391 | -0.611277 | 0.513438 | 0.987249 | 1.226124 | 0.240966 | 0.485917 | 1.355615 | -0.480955 | -0.255325 | -0.370864 | 0.107591 | 0 | 1 |
| 183 | 0.290224 | 0.578762 | 0.024629 | 0.119894 | 0.626180 | 1.025427 | 0.180541 | -0.504388 | -1.085411 | -1.413825 | 0.811722 | 0.640653 | 0.433677 | 0 | 1 |
| 184 | 0.086408 | -1.394139 | -0.501233 | 1.251905 | -0.481983 | 0.026482 | -1.317983 | -0.580623 | -0.160381 | -0.718194 | 0.110108 | -0.183905 | 0.074891 | 2 | 1 |
| 185 | 0.024909 | -0.713904 | -1.235134 | -0.194562 | 0.155358 | -0.586587 | -0.455970 | 0.577457 | 1.172268 | 0.468799 | 0.500130 | 1.133624 | 0.192845 | 2 | 1 |
186 rows × 15 columns
In [129]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[129]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82cc917b8>
In [237]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [282]:
X = df_n_ps_std_mfcc[4]
In [283]:
y = df_n_ps[4]['chosen']
In [284]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [285]:
X_train.shape
Out[285]:
(164, 13)
In [135]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [136]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [137]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [138]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20, 20, 20), 'learning_rate_init': 0.003, 'max_iter': 400}, que permiten obtener un Accuracy de 72.56% y un Kappa del 45.12
Tiempo total: 20.25 minutos
In [286]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20,20,20]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.003
epochs = 400
In [287]:
input_tensor = Input(shape = (n0,))
In [288]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [289]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [290]:
model.summary()
Model: "model_13" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_13 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_41 (Dense) (None, 20) 280 _________________________________________________________________ dense_42 (Dense) (None, 20) 420 _________________________________________________________________ dense_43 (Dense) (None, 20) 420 _________________________________________________________________ dense_44 (Dense) (None, 1) 21 ================================================================= Total params: 1,141 Trainable params: 1,141 Non-trainable params: 0 _________________________________________________________________
In [291]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/400 164/164 [==============================] - 0s 1ms/step - loss: 0.7168 - accuracy: 0.4817 - val_loss: 0.6811 - val_accuracy: 0.5636 Epoch 2/400 164/164 [==============================] - 0s 85us/step - loss: 0.6669 - accuracy: 0.5854 - val_loss: 0.6870 - val_accuracy: 0.6000 Epoch 3/400 164/164 [==============================] - 0s 98us/step - loss: 0.6599 - accuracy: 0.6220 - val_loss: 0.7104 - val_accuracy: 0.5818 Epoch 4/400 164/164 [==============================] - 0s 98us/step - loss: 0.6420 - accuracy: 0.6341 - val_loss: 0.7219 - val_accuracy: 0.5636 Epoch 5/400 164/164 [==============================] - 0s 85us/step - loss: 0.6310 - accuracy: 0.6829 - val_loss: 0.7212 - val_accuracy: 0.5636 Epoch 6/400 164/164 [==============================] - 0s 91us/step - loss: 0.6193 - accuracy: 0.6951 - val_loss: 0.7314 - val_accuracy: 0.4727 Epoch 7/400 164/164 [==============================] - 0s 85us/step - loss: 0.6119 - accuracy: 0.7134 - val_loss: 0.7587 - val_accuracy: 0.4182 Epoch 8/400 164/164 [==============================] - 0s 85us/step - loss: 0.6074 - accuracy: 0.6829 - val_loss: 0.8125 - val_accuracy: 0.4182 Epoch 9/400 164/164 [==============================] - 0s 85us/step - loss: 0.5974 - accuracy: 0.6768 - val_loss: 0.8539 - val_accuracy: 0.3455 Epoch 10/400 164/164 [==============================] - 0s 85us/step - loss: 0.5908 - accuracy: 0.6707 - val_loss: 0.8662 - val_accuracy: 0.3273 Epoch 11/400 164/164 [==============================] - 0s 85us/step - loss: 0.5776 - accuracy: 0.6951 - val_loss: 0.8581 - val_accuracy: 0.3455 Epoch 12/400 164/164 [==============================] - 0s 79us/step - loss: 0.5681 - accuracy: 0.7256 - val_loss: 0.8630 - val_accuracy: 0.4000 Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 13/400 164/164 [==============================] - 0s 85us/step - loss: 0.5613 - accuracy: 0.7195 - val_loss: 0.8672 - val_accuracy: 0.4182 Epoch 14/400 164/164 [==============================] - 0s 91us/step - loss: 0.5581 - accuracy: 0.7195 - val_loss: 0.8675 - val_accuracy: 0.4000 Epoch 15/400 164/164 [==============================] - 0s 98us/step - loss: 0.5507 - accuracy: 0.7195 - val_loss: 0.8727 - val_accuracy: 0.3818 Epoch 16/400 164/164 [==============================] - 0s 91us/step - loss: 0.5440 - accuracy: 0.7317 - val_loss: 0.8778 - val_accuracy: 0.3818 Epoch 17/400 164/164 [==============================] - 0s 91us/step - loss: 0.5369 - accuracy: 0.7317 - val_loss: 0.8860 - val_accuracy: 0.3818 Epoch 18/400 164/164 [==============================] - 0s 91us/step - loss: 0.5301 - accuracy: 0.7378 - val_loss: 0.8864 - val_accuracy: 0.3455 Epoch 19/400 164/164 [==============================] - 0s 91us/step - loss: 0.5248 - accuracy: 0.7317 - val_loss: 0.8862 - val_accuracy: 0.3455 Epoch 20/400 164/164 [==============================] - 0s 98us/step - loss: 0.5199 - accuracy: 0.7439 - val_loss: 0.8968 - val_accuracy: 0.3636 Epoch 21/400 164/164 [==============================] - 0s 85us/step - loss: 0.5140 - accuracy: 0.7439 - val_loss: 0.9013 - val_accuracy: 0.3455 Epoch 22/400 164/164 [==============================] - 0s 91us/step - loss: 0.5075 - accuracy: 0.7439 - val_loss: 0.9059 - val_accuracy: 0.4182 Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 23/400 164/164 [==============================] - 0s 98us/step - loss: 0.5032 - accuracy: 0.7439 - val_loss: 0.9097 - val_accuracy: 0.4182 Epoch 24/400 164/164 [==============================] - 0s 91us/step - loss: 0.4991 - accuracy: 0.7500 - val_loss: 0.9164 - val_accuracy: 0.4000 Epoch 25/400 164/164 [==============================] - 0s 85us/step - loss: 0.4963 - accuracy: 0.7500 - val_loss: 0.9207 - val_accuracy: 0.4000 Epoch 26/400 164/164 [==============================] - 0s 110us/step - loss: 0.4933 - accuracy: 0.7622 - val_loss: 0.9254 - val_accuracy: 0.4182 Epoch 27/400 164/164 [==============================] - 0s 98us/step - loss: 0.4904 - accuracy: 0.7805 - val_loss: 0.9302 - val_accuracy: 0.4182 Epoch 28/400 164/164 [==============================] - 0s 98us/step - loss: 0.4879 - accuracy: 0.7744 - val_loss: 0.9323 - val_accuracy: 0.4000 Epoch 29/400 164/164 [==============================] - 0s 91us/step - loss: 0.4850 - accuracy: 0.7805 - val_loss: 0.9389 - val_accuracy: 0.3818 Epoch 30/400 164/164 [==============================] - 0s 91us/step - loss: 0.4822 - accuracy: 0.7744 - val_loss: 0.9342 - val_accuracy: 0.3636 Epoch 31/400 164/164 [==============================] - 0s 146us/step - loss: 0.4789 - accuracy: 0.7744 - val_loss: 0.9348 - val_accuracy: 0.3636 Epoch 32/400 164/164 [==============================] - 0s 116us/step - loss: 0.4755 - accuracy: 0.7805 - val_loss: 0.9346 - val_accuracy: 0.3636 Epoch 00032: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 33/400 164/164 [==============================] - 0s 104us/step - loss: 0.4732 - accuracy: 0.7805 - val_loss: 0.9364 - val_accuracy: 0.3636 Epoch 34/400 164/164 [==============================] - 0s 85us/step - loss: 0.4716 - accuracy: 0.7805 - val_loss: 0.9369 - val_accuracy: 0.3636 Epoch 35/400 164/164 [==============================] - 0s 85us/step - loss: 0.4704 - accuracy: 0.7805 - val_loss: 0.9399 - val_accuracy: 0.3636 Epoch 36/400 164/164 [==============================] - 0s 98us/step - loss: 0.4688 - accuracy: 0.7805 - val_loss: 0.9395 - val_accuracy: 0.3636 Epoch 37/400 164/164 [==============================] - 0s 91us/step - loss: 0.4676 - accuracy: 0.7805 - val_loss: 0.9403 - val_accuracy: 0.3636 Epoch 38/400 164/164 [==============================] - 0s 98us/step - loss: 0.4658 - accuracy: 0.7805 - val_loss: 0.9409 - val_accuracy: 0.3636 Epoch 39/400 164/164 [==============================] - 0s 98us/step - loss: 0.4648 - accuracy: 0.7805 - val_loss: 0.9430 - val_accuracy: 0.3636 Epoch 40/400 164/164 [==============================] - 0s 85us/step - loss: 0.4633 - accuracy: 0.7805 - val_loss: 0.9435 - val_accuracy: 0.3636 Epoch 41/400 164/164 [==============================] - 0s 98us/step - loss: 0.4616 - accuracy: 0.7805 - val_loss: 0.9435 - val_accuracy: 0.3636 Epoch 42/400 164/164 [==============================] - 0s 91us/step - loss: 0.4599 - accuracy: 0.7805 - val_loss: 0.9446 - val_accuracy: 0.3818 Epoch 00042: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 43/400 164/164 [==============================] - 0s 128us/step - loss: 0.4583 - accuracy: 0.7866 - val_loss: 0.9457 - val_accuracy: 0.3818 Epoch 44/400 164/164 [==============================] - 0s 91us/step - loss: 0.4573 - accuracy: 0.7927 - val_loss: 0.9477 - val_accuracy: 0.3818 Epoch 45/400 164/164 [==============================] - 0s 91us/step - loss: 0.4568 - accuracy: 0.7866 - val_loss: 0.9489 - val_accuracy: 0.3818 Epoch 46/400 164/164 [==============================] - 0s 91us/step - loss: 0.4562 - accuracy: 0.7866 - val_loss: 0.9483 - val_accuracy: 0.3818 Epoch 47/400 164/164 [==============================] - 0s 79us/step - loss: 0.4553 - accuracy: 0.7866 - val_loss: 0.9480 - val_accuracy: 0.3636 Epoch 48/400 164/164 [==============================] - 0s 85us/step - loss: 0.4546 - accuracy: 0.7866 - val_loss: 0.9487 - val_accuracy: 0.3636 Epoch 49/400 164/164 [==============================] - 0s 98us/step - loss: 0.4540 - accuracy: 0.7866 - val_loss: 0.9498 - val_accuracy: 0.3636 Epoch 50/400 164/164 [==============================] - 0s 122us/step - loss: 0.4530 - accuracy: 0.7927 - val_loss: 0.9504 - val_accuracy: 0.3818 Epoch 51/400 164/164 [==============================] - 0s 110us/step - loss: 0.4523 - accuracy: 0.7927 - val_loss: 0.9492 - val_accuracy: 0.3818 Epoch 52/400 164/164 [==============================] - 0s 110us/step - loss: 0.4519 - accuracy: 0.7866 - val_loss: 0.9489 - val_accuracy: 0.3818 Epoch 00052: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 53/400 164/164 [==============================] - 0s 91us/step - loss: 0.4513 - accuracy: 0.7805 - val_loss: 0.9485 - val_accuracy: 0.3818 Epoch 54/400 164/164 [==============================] - 0s 104us/step - loss: 0.4507 - accuracy: 0.7805 - val_loss: 0.9486 - val_accuracy: 0.3818 Epoch 55/400 164/164 [==============================] - 0s 85us/step - loss: 0.4503 - accuracy: 0.7805 - val_loss: 0.9493 - val_accuracy: 0.3818 Epoch 56/400 164/164 [==============================] - 0s 79us/step - loss: 0.4499 - accuracy: 0.7805 - val_loss: 0.9502 - val_accuracy: 0.3818 Epoch 57/400 164/164 [==============================] - 0s 85us/step - loss: 0.4495 - accuracy: 0.7866 - val_loss: 0.9506 - val_accuracy: 0.3818 Epoch 58/400 164/164 [==============================] - 0s 91us/step - loss: 0.4490 - accuracy: 0.7866 - val_loss: 0.9514 - val_accuracy: 0.3818 Epoch 59/400 164/164 [==============================] - 0s 79us/step - loss: 0.4486 - accuracy: 0.7866 - val_loss: 0.9518 - val_accuracy: 0.3818 Epoch 60/400 164/164 [==============================] - 0s 79us/step - loss: 0.4482 - accuracy: 0.7866 - val_loss: 0.9530 - val_accuracy: 0.4000 Epoch 61/400 164/164 [==============================] - 0s 79us/step - loss: 0.4477 - accuracy: 0.7866 - val_loss: 0.9536 - val_accuracy: 0.4000 Epoch 62/400 164/164 [==============================] - 0s 122us/step - loss: 0.4474 - accuracy: 0.7927 - val_loss: 0.9528 - val_accuracy: 0.3818 Epoch 00062: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 63/400 164/164 [==============================] - 0s 104us/step - loss: 0.4470 - accuracy: 0.7927 - val_loss: 0.9529 - val_accuracy: 0.3818 Epoch 64/400 164/164 [==============================] - 0s 110us/step - loss: 0.4467 - accuracy: 0.7927 - val_loss: 0.9535 - val_accuracy: 0.3818 Epoch 65/400 164/164 [==============================] - 0s 98us/step - loss: 0.4465 - accuracy: 0.7927 - val_loss: 0.9538 - val_accuracy: 0.3818 Epoch 66/400 164/164 [==============================] - 0s 85us/step - loss: 0.4463 - accuracy: 0.7988 - val_loss: 0.9538 - val_accuracy: 0.4000 Epoch 67/400 164/164 [==============================] - 0s 98us/step - loss: 0.4461 - accuracy: 0.8049 - val_loss: 0.9538 - val_accuracy: 0.4000 Epoch 68/400 164/164 [==============================] - 0s 91us/step - loss: 0.4459 - accuracy: 0.7988 - val_loss: 0.9535 - val_accuracy: 0.4000 Epoch 69/400 164/164 [==============================] - 0s 98us/step - loss: 0.4457 - accuracy: 0.7988 - val_loss: 0.9531 - val_accuracy: 0.4000 Epoch 70/400 164/164 [==============================] - 0s 98us/step - loss: 0.4455 - accuracy: 0.7988 - val_loss: 0.9530 - val_accuracy: 0.4000 Epoch 71/400 164/164 [==============================] - 0s 91us/step - loss: 0.4453 - accuracy: 0.7988 - val_loss: 0.9528 - val_accuracy: 0.4000 Epoch 72/400 164/164 [==============================] - 0s 79us/step - loss: 0.4451 - accuracy: 0.7988 - val_loss: 0.9526 - val_accuracy: 0.4000 Epoch 00072: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 73/400 164/164 [==============================] - 0s 79us/step - loss: 0.4449 - accuracy: 0.7988 - val_loss: 0.9525 - val_accuracy: 0.4000 Epoch 74/400 164/164 [==============================] - 0s 79us/step - loss: 0.4448 - accuracy: 0.7988 - val_loss: 0.9525 - val_accuracy: 0.4000 Epoch 75/400 164/164 [==============================] - 0s 91us/step - loss: 0.4447 - accuracy: 0.7988 - val_loss: 0.9525 - val_accuracy: 0.4000 Epoch 76/400 164/164 [==============================] - 0s 85us/step - loss: 0.4447 - accuracy: 0.7988 - val_loss: 0.9525 - val_accuracy: 0.4000 Epoch 77/400 164/164 [==============================] - 0s 91us/step - loss: 0.4446 - accuracy: 0.7988 - val_loss: 0.9526 - val_accuracy: 0.4000 Epoch 78/400 164/164 [==============================] - 0s 91us/step - loss: 0.4445 - accuracy: 0.7988 - val_loss: 0.9527 - val_accuracy: 0.4000 Epoch 79/400 164/164 [==============================] - 0s 91us/step - loss: 0.4444 - accuracy: 0.7988 - val_loss: 0.9528 - val_accuracy: 0.4182 Epoch 80/400 164/164 [==============================] - 0s 85us/step - loss: 0.4443 - accuracy: 0.7988 - val_loss: 0.9529 - val_accuracy: 0.4000 Epoch 81/400 164/164 [==============================] - 0s 98us/step - loss: 0.4442 - accuracy: 0.7988 - val_loss: 0.9531 - val_accuracy: 0.4000 Epoch 82/400 164/164 [==============================] - 0s 91us/step - loss: 0.4441 - accuracy: 0.7988 - val_loss: 0.9533 - val_accuracy: 0.4000 Epoch 00082: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 83/400 164/164 [==============================] - 0s 85us/step - loss: 0.4440 - accuracy: 0.7988 - val_loss: 0.9533 - val_accuracy: 0.4182 Epoch 84/400 164/164 [==============================] - 0s 79us/step - loss: 0.4439 - accuracy: 0.7988 - val_loss: 0.9534 - val_accuracy: 0.4182 Epoch 85/400 164/164 [==============================] - 0s 85us/step - loss: 0.4439 - accuracy: 0.7988 - val_loss: 0.9536 - val_accuracy: 0.4182 Epoch 86/400 164/164 [==============================] - 0s 79us/step - loss: 0.4438 - accuracy: 0.8049 - val_loss: 0.9537 - val_accuracy: 0.4182 Epoch 87/400 164/164 [==============================] - 0s 91us/step - loss: 0.4438 - accuracy: 0.8049 - val_loss: 0.9538 - val_accuracy: 0.4182 Epoch 88/400 164/164 [==============================] - 0s 98us/step - loss: 0.4437 - accuracy: 0.8049 - val_loss: 0.9536 - val_accuracy: 0.4182 Epoch 89/400 164/164 [==============================] - 0s 79us/step - loss: 0.4437 - accuracy: 0.8049 - val_loss: 0.9535 - val_accuracy: 0.4182 Epoch 90/400 164/164 [==============================] - 0s 73us/step - loss: 0.4436 - accuracy: 0.8049 - val_loss: 0.9534 - val_accuracy: 0.4182 Epoch 91/400 164/164 [==============================] - 0s 79us/step - loss: 0.4436 - accuracy: 0.8049 - val_loss: 0.9535 - val_accuracy: 0.4182 Epoch 92/400 164/164 [==============================] - 0s 73us/step - loss: 0.4435 - accuracy: 0.8049 - val_loss: 0.9535 - val_accuracy: 0.4182 Epoch 00092: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 93/400 164/164 [==============================] - 0s 140us/step - loss: 0.4435 - accuracy: 0.8049 - val_loss: 0.9536 - val_accuracy: 0.4182 Epoch 94/400 164/164 [==============================] - 0s 104us/step - loss: 0.4435 - accuracy: 0.8049 - val_loss: 0.9536 - val_accuracy: 0.4182 Epoch 95/400 164/164 [==============================] - 0s 98us/step - loss: 0.4434 - accuracy: 0.8049 - val_loss: 0.9536 - val_accuracy: 0.4182 Epoch 96/400 164/164 [==============================] - 0s 104us/step - loss: 0.4434 - accuracy: 0.8049 - val_loss: 0.9537 - val_accuracy: 0.4182 Epoch 97/400 164/164 [==============================] - 0s 116us/step - loss: 0.4434 - accuracy: 0.8049 - val_loss: 0.9538 - val_accuracy: 0.4182 Epoch 98/400 164/164 [==============================] - 0s 98us/step - loss: 0.4434 - accuracy: 0.8049 - val_loss: 0.9538 - val_accuracy: 0.4182 Epoch 99/400 164/164 [==============================] - 0s 98us/step - loss: 0.4433 - accuracy: 0.8049 - val_loss: 0.9539 - val_accuracy: 0.4182 Epoch 100/400 164/164 [==============================] - 0s 104us/step - loss: 0.4433 - accuracy: 0.8049 - val_loss: 0.9540 - val_accuracy: 0.4182 Epoch 101/400 164/164 [==============================] - 0s 122us/step - loss: 0.4433 - accuracy: 0.8049 - val_loss: 0.9540 - val_accuracy: 0.4182 Epoch 102/400 164/164 [==============================] - 0s 97us/step - loss: 0.4433 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 00102: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 103/400 164/164 [==============================] - 0s 85us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 104/400 164/164 [==============================] - 0s 91us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 105/400 164/164 [==============================] - 0s 98us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 106/400 164/164 [==============================] - 0s 85us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 107/400 164/164 [==============================] - 0s 91us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 108/400 164/164 [==============================] - 0s 91us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 109/400 164/164 [==============================] - 0s 98us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 110/400 164/164 [==============================] - 0s 85us/step - loss: 0.4432 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 111/400 164/164 [==============================] - 0s 79us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 112/400 164/164 [==============================] - 0s 98us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00112: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 113/400 164/164 [==============================] - 0s 104us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 114/400 164/164 [==============================] - 0s 110us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 115/400 164/164 [==============================] - 0s 110us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 116/400 164/164 [==============================] - 0s 91us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 117/400 164/164 [==============================] - 0s 79us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 118/400 164/164 [==============================] - 0s 91us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 119/400 164/164 [==============================] - 0s 85us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 120/400 164/164 [==============================] - 0s 98us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 121/400 164/164 [==============================] - 0s 85us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 122/400 164/164 [==============================] - 0s 79us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 00122: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 123/400 164/164 [==============================] - 0s 85us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 124/400 164/164 [==============================] - 0s 79us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 125/400 164/164 [==============================] - 0s 98us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 126/400 164/164 [==============================] - 0s 85us/step - loss: 0.4431 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 127/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 128/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 129/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 130/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 131/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 132/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 00132: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 133/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 134/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 135/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 136/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 137/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 138/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 139/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 140/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 141/400 164/164 [==============================] - 0s 122us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9541 - val_accuracy: 0.4182 Epoch 142/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00142: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 143/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 144/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 145/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 146/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 147/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 148/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 149/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 150/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 151/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 152/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00152: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 153/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 154/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 155/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 156/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 157/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 158/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 159/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 160/400 164/164 [==============================] - 0s 97us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 161/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 162/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00162: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 163/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 164/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 165/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 166/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 167/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 168/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 169/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 170/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 171/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 172/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00172: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 173/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 174/400 164/164 [==============================] - 0s 122us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 175/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 176/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 177/400 164/164 [==============================] - 0s 110us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 178/400 164/164 [==============================] - 0s 97us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 179/400 164/164 [==============================] - 0s 97us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 180/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 181/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 182/400 164/164 [==============================] - 0s 61us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00182: ReduceLROnPlateau reducing learning rate to 1.1444091896350983e-08. Epoch 183/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 184/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 185/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 186/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 187/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 188/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 189/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 190/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 191/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 192/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00192: ReduceLROnPlateau reducing learning rate to 5.7220459481754915e-09. Epoch 193/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 194/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 195/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 196/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 197/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 198/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 199/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 200/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 201/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 202/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00202: ReduceLROnPlateau reducing learning rate to 2.8610229740877458e-09. Epoch 203/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 204/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 205/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 206/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 207/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 208/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 209/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 210/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 211/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 212/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00212: ReduceLROnPlateau reducing learning rate to 1.4305114870438729e-09. Epoch 213/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 214/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 215/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 216/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 217/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 218/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 219/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 220/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 221/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 222/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00222: ReduceLROnPlateau reducing learning rate to 7.152557435219364e-10. Epoch 223/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 224/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 225/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 226/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 227/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 228/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 229/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 230/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 231/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 232/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00232: ReduceLROnPlateau reducing learning rate to 3.576278717609682e-10. Epoch 233/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 234/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 235/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 236/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 237/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 238/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 239/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 240/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 241/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 242/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00242: ReduceLROnPlateau reducing learning rate to 1.788139358804841e-10. Epoch 243/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 244/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 245/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 246/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 247/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 248/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 249/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 250/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 251/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 252/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00252: ReduceLROnPlateau reducing learning rate to 8.940696794024205e-11. Epoch 253/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 254/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 255/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 256/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 257/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 258/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 259/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 260/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 261/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 262/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00262: ReduceLROnPlateau reducing learning rate to 4.470348397012103e-11. Epoch 263/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 264/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 265/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 266/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 267/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 268/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 269/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 270/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 271/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 272/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00272: ReduceLROnPlateau reducing learning rate to 2.2351741985060514e-11. Epoch 273/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 274/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 275/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 276/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 277/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 278/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 279/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 280/400 164/164 [==============================] - 0s 104us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 281/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 282/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00282: ReduceLROnPlateau reducing learning rate to 1.1175870992530257e-11. Epoch 283/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 284/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 285/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 286/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 287/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 288/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 289/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 290/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 291/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 292/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00292: ReduceLROnPlateau reducing learning rate to 5.5879354962651284e-12. Epoch 293/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 294/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 295/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 296/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 297/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 298/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 299/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 300/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 301/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 302/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00302: ReduceLROnPlateau reducing learning rate to 2.7939677481325642e-12. Epoch 303/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 304/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 305/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 306/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 307/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 308/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 309/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 310/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 311/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 312/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00312: ReduceLROnPlateau reducing learning rate to 1.3969838740662821e-12. Epoch 313/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 314/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 315/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 316/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 317/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 318/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 319/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 320/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 321/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 322/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00322: ReduceLROnPlateau reducing learning rate to 6.984919370331411e-13. Epoch 323/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 324/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 325/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 326/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 327/400 164/164 [==============================] - ETA: 0s - loss: 0.4129 - accuracy: 0.87 - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 328/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 329/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 330/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 331/400 164/164 [==============================] - 0s 104us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 332/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00332: ReduceLROnPlateau reducing learning rate to 3.4924596851657053e-13. Epoch 333/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 334/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 335/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 336/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 337/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 338/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 339/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 340/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 341/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 342/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00342: ReduceLROnPlateau reducing learning rate to 1.7462298425828526e-13. Epoch 343/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 344/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 345/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 346/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 347/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 348/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 349/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 350/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 351/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 352/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00352: ReduceLROnPlateau reducing learning rate to 8.731149212914263e-14. Epoch 353/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 354/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 355/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 356/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 357/400 164/164 [==============================] - 0s 61us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 358/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 359/400 164/164 [==============================] - 0s 61us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 360/400 164/164 [==============================] - 0s 61us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 361/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 362/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00362: ReduceLROnPlateau reducing learning rate to 4.3655746064571316e-14. Epoch 363/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 364/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 365/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 366/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 367/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 368/400 164/164 [==============================] - 0s 61us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 369/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 370/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 371/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 372/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00372: ReduceLROnPlateau reducing learning rate to 2.1827873032285658e-14. Epoch 373/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 374/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 375/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 376/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 377/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 378/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 379/400 164/164 [==============================] - 0s 85us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 380/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 381/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 382/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00382: ReduceLROnPlateau reducing learning rate to 1.0913936516142829e-14. Epoch 383/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 384/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 385/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 386/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 387/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 388/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 389/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 390/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 391/400 164/164 [==============================] - 0s 79us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 392/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 00392: ReduceLROnPlateau reducing learning rate to 5.4569682580714145e-15. Epoch 393/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 394/400 164/164 [==============================] - 0s 67us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 395/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 396/400 164/164 [==============================] - 0s 110us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 397/400 164/164 [==============================] - 0s 98us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 398/400 164/164 [==============================] - 0s 91us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 399/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182 Epoch 400/400 164/164 [==============================] - 0s 73us/step - loss: 0.4430 - accuracy: 0.8049 - val_loss: 0.9542 - val_accuracy: 0.4182
In [292]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 400)
In [293]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 55us/step test loss: 0.9541770685802806, test accuracy: 0.41818180680274963
In [294]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.4260752688172043
In [295]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: -0.13989637305699487
KMeans¶
In [149]:
X
Out[149]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.992062 | -0.477172 | -1.079451 | -2.369470 | -1.705431 | -0.098594 | -0.281836 | -1.432001 | -0.898623 | 0.130446 | -0.024683 | -0.312128 | 0.020392 |
| 1 | 0.843575 | -0.507672 | -0.731713 | -0.334904 | 1.442336 | -0.491141 | -0.266416 | -0.511246 | 1.004414 | 0.558777 | 0.127114 | -1.667555 | 0.835458 |
| 2 | 0.816922 | -0.263544 | 0.639646 | -0.865417 | 1.276602 | -0.245238 | 0.106722 | -0.761365 | -0.170481 | -1.443667 | -0.451102 | 1.196430 | -0.037846 |
| 3 | 4.368525 | 0.851784 | -0.671158 | -0.128467 | 2.141169 | -0.472725 | -1.437233 | -1.858760 | 1.581800 | -0.145852 | 0.107228 | 1.458238 | 1.666081 |
| 4 | 0.001312 | 0.535305 | -0.648296 | 0.221414 | 0.549478 | 0.736878 | -0.439538 | -0.138787 | 0.584258 | 0.095671 | 1.901833 | 2.909252 | 1.802578 |
| 5 | -0.236754 | 0.488978 | 0.203743 | 0.088401 | -0.151814 | 0.811707 | -0.092973 | 0.153518 | -0.936863 | 0.354100 | 0.123352 | 1.318569 | 1.097711 |
| 6 | -0.842496 | 0.742173 | 0.068601 | 1.394492 | -0.276167 | 1.301853 | 0.336343 | 1.077540 | -1.118983 | 1.688235 | -0.103661 | 1.224883 | 0.350956 |
| 7 | -0.952702 | 1.078642 | -0.563379 | -0.018149 | -0.073042 | -0.591301 | -1.392389 | 0.209234 | 0.725065 | 0.064350 | 0.034449 | 0.581953 | 2.151966 |
| 8 | 0.046457 | -0.093025 | -0.804385 | 0.542662 | -0.130939 | 0.042792 | 1.198959 | -0.559116 | 0.017192 | -0.249308 | 0.747851 | -0.035599 | 0.995166 |
| 9 | -0.781158 | 0.099463 | 0.196737 | 2.462131 | 0.316140 | -0.369698 | 2.196715 | -0.800443 | 2.137687 | 1.438443 | 0.055279 | -0.284437 | 1.702942 |
| 10 | -0.906167 | 0.568017 | 0.700382 | 2.876646 | -0.809125 | -0.491839 | 1.801564 | -2.406947 | 1.939246 | 1.397556 | 0.709408 | -0.423394 | 1.773713 |
| 11 | 1.172687 | 1.292213 | -0.402038 | 0.087342 | 0.324539 | 0.973336 | -0.548282 | 0.781195 | 0.846038 | 0.464514 | -1.030463 | -0.559243 | 0.168727 |
| 12 | 0.367875 | 1.949889 | 0.516382 | 0.657124 | -0.534306 | 0.575187 | -0.750861 | 0.247200 | -0.232297 | 0.332174 | -0.426787 | 0.318763 | 0.083316 |
| 13 | 1.270520 | 1.194102 | 0.267933 | 0.676186 | 0.394734 | -0.709975 | -0.047626 | 1.113385 | 0.339962 | 0.424937 | -0.528480 | 0.671225 | 0.078062 |
| 14 | -0.095931 | 0.792392 | 0.626113 | 0.189989 | 0.315198 | -0.175744 | 0.011713 | -0.072196 | 0.742338 | 0.974567 | 0.935685 | 0.083454 | 0.970157 |
| 15 | -0.322645 | 0.977766 | 0.685697 | 0.670670 | 0.997903 | 0.619018 | 0.498110 | -0.016728 | 0.445370 | -0.102204 | 0.199517 | -0.315303 | 0.347920 |
| 16 | 0.565974 | 0.440551 | 0.402995 | 1.815814 | 1.906139 | 1.105013 | 1.256180 | 0.907086 | 0.592851 | -0.159427 | 1.013051 | -0.620202 | 1.259932 |
| 17 | -0.863540 | 0.887127 | 1.387720 | -0.082168 | -0.694633 | -0.810037 | 1.251697 | -0.443532 | 0.307506 | 0.253798 | -0.292483 | 0.030812 | 0.176350 |
| 18 | -0.822258 | -0.630193 | -0.672294 | -0.279417 | -0.731983 | -1.510167 | -1.393705 | -0.161872 | 0.722297 | 0.910604 | -0.610303 | 0.380547 | 1.296315 |
| 19 | -0.889164 | 0.641922 | 2.278761 | 0.190213 | -0.341231 | -0.624107 | 1.228820 | -0.549441 | -0.662942 | 0.481866 | -0.541347 | -1.061735 | -0.122227 |
| 20 | 0.795964 | 0.484784 | 0.898919 | 0.027625 | 0.415359 | 0.271286 | 0.366966 | -0.498975 | 0.300352 | 0.216702 | 0.361195 | -0.771976 | 0.085971 |
| 21 | 0.168183 | -0.077353 | 1.019887 | -0.637065 | 0.731534 | 0.877245 | 1.225125 | -0.566997 | -0.452222 | -1.105384 | 0.185636 | -0.782808 | -0.224975 |
| 22 | 0.510023 | -0.099060 | 0.064384 | -0.039933 | 0.786951 | 0.119530 | -0.259052 | -0.881354 | -0.113425 | 1.191274 | 0.335443 | -0.189618 | -0.337688 |
| 23 | 0.216210 | -0.069447 | 0.974822 | -0.626273 | 0.835854 | 0.914236 | 1.226463 | -0.369525 | -0.398299 | -1.146613 | 0.026274 | -0.944475 | -0.192948 |
| 24 | -0.239273 | -0.518568 | -0.127834 | 0.045011 | 0.403223 | 0.368253 | -0.584902 | -0.905436 | -0.405699 | 0.129383 | 0.809611 | -0.174138 | -0.115393 |
| 25 | -1.241907 | 1.355534 | -0.693470 | 0.793789 | 0.606007 | 0.930263 | 0.009323 | -0.712463 | 0.037916 | -0.182143 | 1.212760 | -0.083882 | 0.639662 |
| 26 | -0.847436 | 1.180146 | -0.489592 | 1.189572 | -0.457645 | -0.163979 | -0.010812 | -0.765561 | -0.347488 | -0.216575 | 0.804302 | -0.236378 | 0.481212 |
| 27 | -0.378383 | 1.017722 | -1.812001 | 0.443514 | 0.583209 | 1.709730 | 0.715521 | -0.076610 | 0.416120 | 0.013436 | 0.420025 | -0.925263 | 0.626400 |
| 28 | 0.245370 | 1.187084 | 1.056929 | 2.013063 | -0.505622 | 1.228583 | -1.158143 | 0.622932 | 0.113512 | 0.948397 | 0.008252 | 1.035839 | -0.691702 |
| 29 | -0.623386 | 1.368898 | 1.216933 | 1.961377 | 0.744541 | 1.555516 | -1.205283 | -0.252995 | -0.325624 | 0.538668 | 0.197646 | 0.356450 | -0.219812 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.565077 | 0.809784 | 0.557457 | 0.815038 | 0.823053 | -0.931359 | -0.039244 | -0.199068 | 0.083690 | -0.235063 | -0.030800 | -0.564557 | -0.253507 |
| 190 | -0.602848 | 0.638838 | 0.763481 | -0.424641 | -0.810302 | -0.951734 | -0.732024 | -0.504038 | 0.379372 | 0.748895 | -0.593820 | -0.772491 | 0.175752 |
| 191 | -1.094031 | -0.896961 | 0.400325 | -1.635971 | -1.099938 | -1.091799 | -0.593281 | 0.890889 | 0.984647 | 0.584509 | 0.318496 | 0.175062 | -0.783524 |
| 192 | -0.348357 | 0.944340 | 0.239675 | 0.003612 | -1.370450 | -0.996597 | -0.616405 | 0.161481 | -0.258760 | 0.534721 | -0.431338 | 0.376456 | -1.623026 |
| 193 | 2.110671 | -1.005236 | 0.268022 | 0.459390 | -1.985350 | 0.405677 | -0.361571 | -1.272053 | -0.873345 | 2.111218 | -0.246708 | 0.798456 | 1.067252 |
| 194 | 1.222194 | -1.600122 | -1.149302 | 0.230839 | -0.213026 | -1.572114 | 0.486447 | -0.770701 | 0.244895 | 2.689114 | -2.296486 | 0.718338 | -1.220356 |
| 195 | -0.509789 | -0.757711 | 0.189267 | 0.516644 | 0.750906 | -1.485714 | 2.485824 | -1.204754 | -3.373113 | -0.450016 | -1.091178 | -0.474728 | -0.522197 |
| 196 | 0.194175 | -0.618441 | -1.090420 | 0.233017 | -1.492602 | -0.342192 | -1.612833 | 0.714990 | 0.072755 | -0.026932 | 0.464029 | 0.212333 | 1.204262 |
| 197 | 0.297635 | -0.727616 | -1.927078 | -0.145347 | -0.990256 | 0.052935 | -1.791108 | -0.351333 | -0.064903 | 0.201842 | 1.581215 | 1.084453 | -0.168841 |
| 198 | -0.271030 | -0.575137 | -1.005334 | -0.238705 | -0.931830 | -1.319114 | -0.668613 | 0.510822 | 0.209623 | 0.487577 | 0.154874 | 0.133768 | 1.259548 |
| 199 | 0.059096 | -0.370313 | -0.760047 | 0.706270 | -2.488266 | -1.336692 | -0.683584 | 0.436366 | -0.150281 | -0.711308 | -0.851205 | 0.253942 | -0.052516 |
| 200 | 0.147539 | -0.233608 | -0.578016 | 0.870637 | -2.418094 | -1.286070 | -0.692623 | 0.342693 | 0.015890 | -0.795418 | -1.221248 | 0.309493 | -0.526480 |
| 201 | -0.076214 | -1.055629 | 0.159389 | -0.403318 | -0.111273 | -1.325990 | -0.867502 | 0.519381 | 0.192007 | -0.024629 | 0.220420 | 0.551046 | 0.399728 |
| 202 | 1.468986 | 0.518464 | 1.475456 | -1.400891 | 0.408186 | -1.831201 | 1.474742 | 0.566660 | -0.403197 | -1.295176 | -0.443787 | -1.884346 | -1.993491 |
| 203 | -1.739107 | 0.192104 | -0.670709 | -1.236237 | -1.672915 | -0.680127 | 0.027148 | 0.524909 | 1.865754 | -0.634310 | -0.607429 | -1.471191 | -0.632982 |
| 204 | -0.663868 | -0.862566 | -0.329803 | -0.857680 | 0.167824 | -0.013328 | 0.176565 | 0.125832 | 0.609671 | -1.296827 | -0.435986 | -1.341223 | -0.977207 |
| 205 | -0.739818 | -0.668220 | -0.077479 | 0.026286 | 0.027801 | 0.040659 | -0.161646 | -1.046948 | -1.248976 | -0.449243 | 1.046834 | 1.381194 | 1.646325 |
| 206 | 0.475752 | 0.695473 | -0.072097 | 1.081397 | -0.366985 | -2.008080 | 0.515734 | 0.005330 | 1.193800 | -0.841825 | -2.650200 | -3.862624 | -2.115507 |
| 207 | -1.331365 | -1.632552 | -0.876636 | 0.076190 | 1.187799 | 1.138590 | 1.235955 | 1.583447 | 0.890342 | -1.587964 | 0.546109 | 1.565567 | 1.756993 |
| 208 | -0.397476 | 0.090963 | 1.217996 | 0.773741 | 1.107204 | -1.125870 | -0.915396 | -1.130561 | -1.914456 | -0.664474 | -0.226576 | 0.112420 | 0.235011 |
| 209 | -0.465823 | -1.372705 | -0.445436 | 0.316510 | -1.492946 | -1.103783 | 0.353513 | -0.311377 | -1.095388 | -0.615078 | -0.585868 | 0.172807 | -0.860564 |
| 210 | -0.594535 | -1.761364 | -1.069906 | -0.502969 | -1.411276 | -0.906350 | -0.559102 | -1.240920 | -2.254196 | -1.206339 | -0.528047 | 0.924112 | 0.472298 |
| 211 | -1.022693 | 0.373374 | -0.104205 | -0.815628 | -0.574733 | 0.906934 | 0.765114 | -0.015386 | 0.110695 | 1.832325 | 0.712557 | -0.951976 | -0.678869 |
| 212 | -0.967902 | 0.155275 | 0.013938 | -0.549105 | -0.907792 | 0.881907 | 0.609589 | -0.135010 | -0.373473 | 1.152134 | 0.386511 | -0.744687 | -0.447017 |
| 213 | -1.238242 | -0.062983 | -0.133082 | -0.158458 | -0.338086 | -0.411874 | 0.964537 | 0.870379 | 0.530337 | 0.858339 | 0.489332 | -1.190977 | -1.340484 |
| 214 | 0.349761 | -1.391267 | -3.069473 | 0.840195 | 1.044391 | -1.052018 | 1.004856 | 1.478511 | 1.210060 | -1.145325 | 2.653757 | 1.937234 | 0.592139 |
| 215 | 0.782819 | -1.300386 | -0.487318 | 0.850960 | -2.046427 | 1.050631 | 0.289069 | 2.400271 | 2.707288 | -0.278238 | 0.152360 | 1.912210 | -0.208225 |
| 216 | 1.847553 | -1.059174 | -0.808403 | 0.400706 | -0.275009 | 0.409744 | -0.141885 | 0.706348 | 0.476002 | 0.990111 | -0.168504 | 0.856440 | -0.395652 |
| 217 | 2.608478 | 0.174234 | 2.534211 | -0.985597 | -0.436400 | 3.751943 | 1.560179 | -2.367095 | 1.272529 | 2.464209 | -0.954336 | 0.310720 | -1.209456 |
| 218 | -0.069569 | 0.418008 | -0.004324 | 1.330358 | 0.365352 | -0.582788 | -0.527444 | -0.298114 | -0.353021 | -1.118883 | -0.459230 | -0.986241 | -0.041010 |
219 rows × 13 columns
In [150]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[150]:
[2847.0, 2572.5760570812117, 2370.209947155015, 2235.6129406180157, 2112.951551625758, 2041.1809211260454, 1982.3615393500422, 1899.0667595696164, 1851.9267246215204, 1760.4468946465518, 1745.79714786859, 1689.1350809615656, 1657.4940102564742, 1625.370413913055]
In [151]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[151]:
[<matplotlib.lines.Line2D at 0x1e82d2ffda0>]
K=3
In [152]:
kmeans_mfcc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[152]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [153]:
kmeans_mfcc.labels_
Out[153]:
array([1, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2,
1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 1, 1, 2, 0, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2,
0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 2,
2, 0, 1, 2, 0, 0, 2, 2, 2, 2, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0,
0, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 2, 0, 1, 2, 2, 2,
1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 0, 2, 0, 2, 2, 2, 0, 2,
2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 2, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1])
In [154]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[154]:
array([1, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2,
1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 1, 1, 2, 0, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2,
0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 2,
2, 0, 1, 2, 0, 0, 2, 2, 2, 2, 1, 1, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0,
0, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 1, 2, 0, 1, 2, 2, 2,
1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 0, 1, 0, 0, 2, 0, 2, 2, 2, 0, 2,
2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 2, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 2, 0, 0, 1, 0, 1])
In [155]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [156]:
X
Out[156]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.992062 | -0.477172 | -1.079451 | -2.369470 | -1.705431 | -0.098594 | -0.281836 | -1.432001 | -0.898623 | 0.130446 | -0.024683 | -0.312128 | 0.020392 | 1 | 0 |
| 1 | 0.843575 | -0.507672 | -0.731713 | -0.334904 | 1.442336 | -0.491141 | -0.266416 | -0.511246 | 1.004414 | 0.558777 | 0.127114 | -1.667555 | 0.835458 | 2 | 0 |
| 2 | 0.816922 | -0.263544 | 0.639646 | -0.865417 | 1.276602 | -0.245238 | 0.106722 | -0.761365 | -0.170481 | -1.443667 | -0.451102 | 1.196430 | -0.037846 | 1 | 0 |
| 3 | 4.368525 | 0.851784 | -0.671158 | -0.128467 | 2.141169 | -0.472725 | -1.437233 | -1.858760 | 1.581800 | -0.145852 | 0.107228 | 1.458238 | 1.666081 | 1 | 0 |
| 4 | 0.001312 | 0.535305 | -0.648296 | 0.221414 | 0.549478 | 0.736878 | -0.439538 | -0.138787 | 0.584258 | 0.095671 | 1.901833 | 2.909252 | 1.802578 | 0 | 0 |
| 5 | -0.236754 | 0.488978 | 0.203743 | 0.088401 | -0.151814 | 0.811707 | -0.092973 | 0.153518 | -0.936863 | 0.354100 | 0.123352 | 1.318569 | 1.097711 | 0 | 0 |
| 6 | -0.842496 | 0.742173 | 0.068601 | 1.394492 | -0.276167 | 1.301853 | 0.336343 | 1.077540 | -1.118983 | 1.688235 | -0.103661 | 1.224883 | 0.350956 | 0 | 0 |
| 7 | -0.952702 | 1.078642 | -0.563379 | -0.018149 | -0.073042 | -0.591301 | -1.392389 | 0.209234 | 0.725065 | 0.064350 | 0.034449 | 0.581953 | 2.151966 | 1 | 0 |
| 8 | 0.046457 | -0.093025 | -0.804385 | 0.542662 | -0.130939 | 0.042792 | 1.198959 | -0.559116 | 0.017192 | -0.249308 | 0.747851 | -0.035599 | 0.995166 | 0 | 0 |
| 9 | -0.781158 | 0.099463 | 0.196737 | 2.462131 | 0.316140 | -0.369698 | 2.196715 | -0.800443 | 2.137687 | 1.438443 | 0.055279 | -0.284437 | 1.702942 | 0 | 0 |
| 10 | -0.906167 | 0.568017 | 0.700382 | 2.876646 | -0.809125 | -0.491839 | 1.801564 | -2.406947 | 1.939246 | 1.397556 | 0.709408 | -0.423394 | 1.773713 | 0 | 0 |
| 11 | 1.172687 | 1.292213 | -0.402038 | 0.087342 | 0.324539 | 0.973336 | -0.548282 | 0.781195 | 0.846038 | 0.464514 | -1.030463 | -0.559243 | 0.168727 | 0 | 0 |
| 12 | 0.367875 | 1.949889 | 0.516382 | 0.657124 | -0.534306 | 0.575187 | -0.750861 | 0.247200 | -0.232297 | 0.332174 | -0.426787 | 0.318763 | 0.083316 | 0 | 0 |
| 13 | 1.270520 | 1.194102 | 0.267933 | 0.676186 | 0.394734 | -0.709975 | -0.047626 | 1.113385 | 0.339962 | 0.424937 | -0.528480 | 0.671225 | 0.078062 | 0 | 0 |
| 14 | -0.095931 | 0.792392 | 0.626113 | 0.189989 | 0.315198 | -0.175744 | 0.011713 | -0.072196 | 0.742338 | 0.974567 | 0.935685 | 0.083454 | 0.970157 | 0 | 0 |
| 15 | -0.322645 | 0.977766 | 0.685697 | 0.670670 | 0.997903 | 0.619018 | 0.498110 | -0.016728 | 0.445370 | -0.102204 | 0.199517 | -0.315303 | 0.347920 | 0 | 0 |
| 16 | 0.565974 | 0.440551 | 0.402995 | 1.815814 | 1.906139 | 1.105013 | 1.256180 | 0.907086 | 0.592851 | -0.159427 | 1.013051 | -0.620202 | 1.259932 | 0 | 0 |
| 17 | -0.863540 | 0.887127 | 1.387720 | -0.082168 | -0.694633 | -0.810037 | 1.251697 | -0.443532 | 0.307506 | 0.253798 | -0.292483 | 0.030812 | 0.176350 | 0 | 0 |
| 18 | -0.822258 | -0.630193 | -0.672294 | -0.279417 | -0.731983 | -1.510167 | -1.393705 | -0.161872 | 0.722297 | 0.910604 | -0.610303 | 0.380547 | 1.296315 | 1 | 0 |
| 19 | -0.889164 | 0.641922 | 2.278761 | 0.190213 | -0.341231 | -0.624107 | 1.228820 | -0.549441 | -0.662942 | 0.481866 | -0.541347 | -1.061735 | -0.122227 | 2 | 0 |
| 20 | 0.795964 | 0.484784 | 0.898919 | 0.027625 | 0.415359 | 0.271286 | 0.366966 | -0.498975 | 0.300352 | 0.216702 | 0.361195 | -0.771976 | 0.085971 | 0 | 0 |
| 21 | 0.168183 | -0.077353 | 1.019887 | -0.637065 | 0.731534 | 0.877245 | 1.225125 | -0.566997 | -0.452222 | -1.105384 | 0.185636 | -0.782808 | -0.224975 | 2 | 0 |
| 22 | 0.510023 | -0.099060 | 0.064384 | -0.039933 | 0.786951 | 0.119530 | -0.259052 | -0.881354 | -0.113425 | 1.191274 | 0.335443 | -0.189618 | -0.337688 | 1 | 0 |
| 23 | 0.216210 | -0.069447 | 0.974822 | -0.626273 | 0.835854 | 0.914236 | 1.226463 | -0.369525 | -0.398299 | -1.146613 | 0.026274 | -0.944475 | -0.192948 | 2 | 0 |
| 24 | -0.239273 | -0.518568 | -0.127834 | 0.045011 | 0.403223 | 0.368253 | -0.584902 | -0.905436 | -0.405699 | 0.129383 | 0.809611 | -0.174138 | -0.115393 | 1 | 0 |
| 25 | -1.241907 | 1.355534 | -0.693470 | 0.793789 | 0.606007 | 0.930263 | 0.009323 | -0.712463 | 0.037916 | -0.182143 | 1.212760 | -0.083882 | 0.639662 | 0 | 0 |
| 26 | -0.847436 | 1.180146 | -0.489592 | 1.189572 | -0.457645 | -0.163979 | -0.010812 | -0.765561 | -0.347488 | -0.216575 | 0.804302 | -0.236378 | 0.481212 | 0 | 0 |
| 27 | -0.378383 | 1.017722 | -1.812001 | 0.443514 | 0.583209 | 1.709730 | 0.715521 | -0.076610 | 0.416120 | 0.013436 | 0.420025 | -0.925263 | 0.626400 | 0 | 0 |
| 28 | 0.245370 | 1.187084 | 1.056929 | 2.013063 | -0.505622 | 1.228583 | -1.158143 | 0.622932 | 0.113512 | 0.948397 | 0.008252 | 1.035839 | -0.691702 | 0 | 0 |
| 29 | -0.623386 | 1.368898 | 1.216933 | 1.961377 | 0.744541 | 1.555516 | -1.205283 | -0.252995 | -0.325624 | 0.538668 | 0.197646 | 0.356450 | -0.219812 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.565077 | 0.809784 | 0.557457 | 0.815038 | 0.823053 | -0.931359 | -0.039244 | -0.199068 | 0.083690 | -0.235063 | -0.030800 | -0.564557 | -0.253507 | 0 | 1 |
| 190 | -0.602848 | 0.638838 | 0.763481 | -0.424641 | -0.810302 | -0.951734 | -0.732024 | -0.504038 | 0.379372 | 0.748895 | -0.593820 | -0.772491 | 0.175752 | 1 | 1 |
| 191 | -1.094031 | -0.896961 | 0.400325 | -1.635971 | -1.099938 | -1.091799 | -0.593281 | 0.890889 | 0.984647 | 0.584509 | 0.318496 | 0.175062 | -0.783524 | 2 | 1 |
| 192 | -0.348357 | 0.944340 | 0.239675 | 0.003612 | -1.370450 | -0.996597 | -0.616405 | 0.161481 | -0.258760 | 0.534721 | -0.431338 | 0.376456 | -1.623026 | 1 | 1 |
| 193 | 2.110671 | -1.005236 | 0.268022 | 0.459390 | -1.985350 | 0.405677 | -0.361571 | -1.272053 | -0.873345 | 2.111218 | -0.246708 | 0.798456 | 1.067252 | 1 | 1 |
| 194 | 1.222194 | -1.600122 | -1.149302 | 0.230839 | -0.213026 | -1.572114 | 0.486447 | -0.770701 | 0.244895 | 2.689114 | -2.296486 | 0.718338 | -1.220356 | 1 | 1 |
| 195 | -0.509789 | -0.757711 | 0.189267 | 0.516644 | 0.750906 | -1.485714 | 2.485824 | -1.204754 | -3.373113 | -0.450016 | -1.091178 | -0.474728 | -0.522197 | 2 | 1 |
| 196 | 0.194175 | -0.618441 | -1.090420 | 0.233017 | -1.492602 | -0.342192 | -1.612833 | 0.714990 | 0.072755 | -0.026932 | 0.464029 | 0.212333 | 1.204262 | 1 | 1 |
| 197 | 0.297635 | -0.727616 | -1.927078 | -0.145347 | -0.990256 | 0.052935 | -1.791108 | -0.351333 | -0.064903 | 0.201842 | 1.581215 | 1.084453 | -0.168841 | 1 | 1 |
| 198 | -0.271030 | -0.575137 | -1.005334 | -0.238705 | -0.931830 | -1.319114 | -0.668613 | 0.510822 | 0.209623 | 0.487577 | 0.154874 | 0.133768 | 1.259548 | 1 | 1 |
| 199 | 0.059096 | -0.370313 | -0.760047 | 0.706270 | -2.488266 | -1.336692 | -0.683584 | 0.436366 | -0.150281 | -0.711308 | -0.851205 | 0.253942 | -0.052516 | 1 | 1 |
| 200 | 0.147539 | -0.233608 | -0.578016 | 0.870637 | -2.418094 | -1.286070 | -0.692623 | 0.342693 | 0.015890 | -0.795418 | -1.221248 | 0.309493 | -0.526480 | 1 | 1 |
| 201 | -0.076214 | -1.055629 | 0.159389 | -0.403318 | -0.111273 | -1.325990 | -0.867502 | 0.519381 | 0.192007 | -0.024629 | 0.220420 | 0.551046 | 0.399728 | 1 | 1 |
| 202 | 1.468986 | 0.518464 | 1.475456 | -1.400891 | 0.408186 | -1.831201 | 1.474742 | 0.566660 | -0.403197 | -1.295176 | -0.443787 | -1.884346 | -1.993491 | 2 | 1 |
| 203 | -1.739107 | 0.192104 | -0.670709 | -1.236237 | -1.672915 | -0.680127 | 0.027148 | 0.524909 | 1.865754 | -0.634310 | -0.607429 | -1.471191 | -0.632982 | 2 | 1 |
| 204 | -0.663868 | -0.862566 | -0.329803 | -0.857680 | 0.167824 | -0.013328 | 0.176565 | 0.125832 | 0.609671 | -1.296827 | -0.435986 | -1.341223 | -0.977207 | 2 | 1 |
| 205 | -0.739818 | -0.668220 | -0.077479 | 0.026286 | 0.027801 | 0.040659 | -0.161646 | -1.046948 | -1.248976 | -0.449243 | 1.046834 | 1.381194 | 1.646325 | 1 | 1 |
| 206 | 0.475752 | 0.695473 | -0.072097 | 1.081397 | -0.366985 | -2.008080 | 0.515734 | 0.005330 | 1.193800 | -0.841825 | -2.650200 | -3.862624 | -2.115507 | 2 | 1 |
| 207 | -1.331365 | -1.632552 | -0.876636 | 0.076190 | 1.187799 | 1.138590 | 1.235955 | 1.583447 | 0.890342 | -1.587964 | 0.546109 | 1.565567 | 1.756993 | 0 | 1 |
| 208 | -0.397476 | 0.090963 | 1.217996 | 0.773741 | 1.107204 | -1.125870 | -0.915396 | -1.130561 | -1.914456 | -0.664474 | -0.226576 | 0.112420 | 0.235011 | 1 | 1 |
| 209 | -0.465823 | -1.372705 | -0.445436 | 0.316510 | -1.492946 | -1.103783 | 0.353513 | -0.311377 | -1.095388 | -0.615078 | -0.585868 | 0.172807 | -0.860564 | 1 | 1 |
| 210 | -0.594535 | -1.761364 | -1.069906 | -0.502969 | -1.411276 | -0.906350 | -0.559102 | -1.240920 | -2.254196 | -1.206339 | -0.528047 | 0.924112 | 0.472298 | 1 | 1 |
| 211 | -1.022693 | 0.373374 | -0.104205 | -0.815628 | -0.574733 | 0.906934 | 0.765114 | -0.015386 | 0.110695 | 1.832325 | 0.712557 | -0.951976 | -0.678869 | 2 | 1 |
| 212 | -0.967902 | 0.155275 | 0.013938 | -0.549105 | -0.907792 | 0.881907 | 0.609589 | -0.135010 | -0.373473 | 1.152134 | 0.386511 | -0.744687 | -0.447017 | 2 | 1 |
| 213 | -1.238242 | -0.062983 | -0.133082 | -0.158458 | -0.338086 | -0.411874 | 0.964537 | 0.870379 | 0.530337 | 0.858339 | 0.489332 | -1.190977 | -1.340484 | 2 | 1 |
| 214 | 0.349761 | -1.391267 | -3.069473 | 0.840195 | 1.044391 | -1.052018 | 1.004856 | 1.478511 | 1.210060 | -1.145325 | 2.653757 | 1.937234 | 0.592139 | 0 | 1 |
| 215 | 0.782819 | -1.300386 | -0.487318 | 0.850960 | -2.046427 | 1.050631 | 0.289069 | 2.400271 | 2.707288 | -0.278238 | 0.152360 | 1.912210 | -0.208225 | 0 | 1 |
| 216 | 1.847553 | -1.059174 | -0.808403 | 0.400706 | -0.275009 | 0.409744 | -0.141885 | 0.706348 | 0.476002 | 0.990111 | -0.168504 | 0.856440 | -0.395652 | 1 | 1 |
| 217 | 2.608478 | 0.174234 | 2.534211 | -0.985597 | -0.436400 | 3.751943 | 1.560179 | -2.367095 | 1.272529 | 2.464209 | -0.954336 | 0.310720 | -1.209456 | 0 | 1 |
| 218 | -0.069569 | 0.418008 | -0.004324 | 1.330358 | 0.365352 | -0.582788 | -0.527444 | -0.298114 | -0.353021 | -1.118883 | -0.459230 | -0.986241 | -0.041010 | 1 | 1 |
219 rows × 15 columns
In [157]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[157]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82d379160>
In [252]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [296]:
X = df_n_ps_std_mfcc[5]
In [297]:
y = df_n_ps[5]['chosen']
In [298]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [299]:
X_train.shape
Out[299]:
(168, 13)
In [163]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [164]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [165]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [166]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.009, 'max_iter': 1000}, que permiten obtener un Accuracy de 73.81% y un Kappa del 36.33
Tiempo total: 24.57 minutos
In [300]:
n0=X_train.shape[1]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [301]:
input_tensor = Input(shape = (n0,))
In [302]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [303]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [304]:
model.summary()
Model: "model_14" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_14 (InputLayer) (None, 13) 0 _________________________________________________________________ dense_45 (Dense) (None, 20) 280 _________________________________________________________________ dense_46 (Dense) (None, 20) 420 _________________________________________________________________ dense_47 (Dense) (None, 20) 420 _________________________________________________________________ dense_48 (Dense) (None, 1) 21 ================================================================= Total params: 1,141 Trainable params: 1,141 Non-trainable params: 0 _________________________________________________________________
In [305]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 168 samples, validate on 57 samples Epoch 1/100 168/168 [==============================] - 0s 1ms/step - loss: 0.6734 - accuracy: 0.5714 - val_loss: 0.6686 - val_accuracy: 0.5789 Epoch 2/100 168/168 [==============================] - 0s 107us/step - loss: 0.6107 - accuracy: 0.6667 - val_loss: 0.6513 - val_accuracy: 0.5614 Epoch 3/100 168/168 [==============================] - 0s 77us/step - loss: 0.5755 - accuracy: 0.7202 - val_loss: 0.6565 - val_accuracy: 0.5965 Epoch 4/100 168/168 [==============================] - 0s 71us/step - loss: 0.5608 - accuracy: 0.7262 - val_loss: 0.6731 - val_accuracy: 0.5965 Epoch 5/100 168/168 [==============================] - 0s 65us/step - loss: 0.5544 - accuracy: 0.7321 - val_loss: 0.6889 - val_accuracy: 0.5789 Epoch 6/100 168/168 [==============================] - 0s 71us/step - loss: 0.5457 - accuracy: 0.7321 - val_loss: 0.6953 - val_accuracy: 0.6316 Epoch 7/100 168/168 [==============================] - 0s 65us/step - loss: 0.5374 - accuracy: 0.7202 - val_loss: 0.6785 - val_accuracy: 0.6491 Epoch 8/100 168/168 [==============================] - 0s 71us/step - loss: 0.5297 - accuracy: 0.7321 - val_loss: 0.6659 - val_accuracy: 0.6316 Epoch 9/100 168/168 [==============================] - 0s 65us/step - loss: 0.5187 - accuracy: 0.7381 - val_loss: 0.6761 - val_accuracy: 0.5965 Epoch 10/100 168/168 [==============================] - 0s 71us/step - loss: 0.5126 - accuracy: 0.7381 - val_loss: 0.6996 - val_accuracy: 0.6140 Epoch 11/100 168/168 [==============================] - 0s 71us/step - loss: 0.5062 - accuracy: 0.7619 - val_loss: 0.7055 - val_accuracy: 0.5614 Epoch 12/100 168/168 [==============================] - 0s 71us/step - loss: 0.4976 - accuracy: 0.7738 - val_loss: 0.6888 - val_accuracy: 0.5614 Epoch 13/100 168/168 [==============================] - 0s 71us/step - loss: 0.4849 - accuracy: 0.7679 - val_loss: 0.6634 - val_accuracy: 0.6316 Epoch 14/100 168/168 [==============================] - 0s 71us/step - loss: 0.4724 - accuracy: 0.7738 - val_loss: 0.6561 - val_accuracy: 0.6316 Epoch 15/100 168/168 [==============================] - 0s 71us/step - loss: 0.4594 - accuracy: 0.7679 - val_loss: 0.6666 - val_accuracy: 0.6316 Epoch 16/100 168/168 [==============================] - 0s 71us/step - loss: 0.4471 - accuracy: 0.7917 - val_loss: 0.6749 - val_accuracy: 0.6140 Epoch 17/100 168/168 [==============================] - 0s 71us/step - loss: 0.4307 - accuracy: 0.8095 - val_loss: 0.6773 - val_accuracy: 0.6667 Epoch 18/100 168/168 [==============================] - 0s 71us/step - loss: 0.4218 - accuracy: 0.8155 - val_loss: 0.6815 - val_accuracy: 0.6491 Epoch 19/100 168/168 [==============================] - 0s 71us/step - loss: 0.4026 - accuracy: 0.8333 - val_loss: 0.6837 - val_accuracy: 0.6491 Epoch 20/100 168/168 [==============================] - 0s 107us/step - loss: 0.3847 - accuracy: 0.8512 - val_loss: 0.7176 - val_accuracy: 0.6316 Epoch 21/100 168/168 [==============================] - 0s 95us/step - loss: 0.3716 - accuracy: 0.8631 - val_loss: 0.7504 - val_accuracy: 0.5965 Epoch 22/100 168/168 [==============================] - 0s 95us/step - loss: 0.3500 - accuracy: 0.8571 - val_loss: 0.7848 - val_accuracy: 0.5965 Epoch 23/100 168/168 [==============================] - 0s 77us/step - loss: 0.3446 - accuracy: 0.8571 - val_loss: 0.7247 - val_accuracy: 0.6667 Epoch 24/100 168/168 [==============================] - 0s 71us/step - loss: 0.3145 - accuracy: 0.9167 - val_loss: 0.6438 - val_accuracy: 0.6667 Epoch 25/100 168/168 [==============================] - 0s 65us/step - loss: 0.2989 - accuracy: 0.8929 - val_loss: 0.6838 - val_accuracy: 0.6491 Epoch 26/100 168/168 [==============================] - 0s 71us/step - loss: 0.2818 - accuracy: 0.9048 - val_loss: 0.7048 - val_accuracy: 0.6140 Epoch 27/100 168/168 [==============================] - 0s 65us/step - loss: 0.2584 - accuracy: 0.9167 - val_loss: 0.6730 - val_accuracy: 0.6667 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 28/100 168/168 [==============================] - 0s 65us/step - loss: 0.2363 - accuracy: 0.9167 - val_loss: 0.6651 - val_accuracy: 0.6667 Epoch 29/100 168/168 [==============================] - 0s 65us/step - loss: 0.2298 - accuracy: 0.9345 - val_loss: 0.6837 - val_accuracy: 0.6491 Epoch 30/100 168/168 [==============================] - 0s 65us/step - loss: 0.2195 - accuracy: 0.9464 - val_loss: 0.7148 - val_accuracy: 0.6491 Epoch 31/100 168/168 [==============================] - 0s 71us/step - loss: 0.2092 - accuracy: 0.9464 - val_loss: 0.7164 - val_accuracy: 0.6667 Epoch 32/100 168/168 [==============================] - 0s 59us/step - loss: 0.1981 - accuracy: 0.9583 - val_loss: 0.7310 - val_accuracy: 0.6316 Epoch 33/100 168/168 [==============================] - 0s 71us/step - loss: 0.1945 - accuracy: 0.9524 - val_loss: 0.7229 - val_accuracy: 0.6491 Epoch 34/100 168/168 [==============================] - 0s 65us/step - loss: 0.1883 - accuracy: 0.9583 - val_loss: 0.6987 - val_accuracy: 0.6491 Epoch 35/100 168/168 [==============================] - 0s 101us/step - loss: 0.1769 - accuracy: 0.9583 - val_loss: 0.6846 - val_accuracy: 0.6842 Epoch 36/100 168/168 [==============================] - 0s 77us/step - loss: 0.1693 - accuracy: 0.9643 - val_loss: 0.6990 - val_accuracy: 0.6842 Epoch 37/100 168/168 [==============================] - 0s 77us/step - loss: 0.1620 - accuracy: 0.9643 - val_loss: 0.7038 - val_accuracy: 0.6667 Epoch 38/100 168/168 [==============================] - 0s 77us/step - loss: 0.1498 - accuracy: 0.9702 - val_loss: 0.7501 - val_accuracy: 0.6667 Epoch 39/100 168/168 [==============================] - 0s 95us/step - loss: 0.1469 - accuracy: 0.9702 - val_loss: 0.7930 - val_accuracy: 0.6667 Epoch 40/100 168/168 [==============================] - 0s 71us/step - loss: 0.1415 - accuracy: 0.9702 - val_loss: 0.7957 - val_accuracy: 0.6667 Epoch 41/100 168/168 [==============================] - 0s 65us/step - loss: 0.1302 - accuracy: 0.9702 - val_loss: 0.7824 - val_accuracy: 0.6842 Epoch 42/100 168/168 [==============================] - 0s 71us/step - loss: 0.1227 - accuracy: 0.9821 - val_loss: 0.7895 - val_accuracy: 0.6842 Epoch 43/100 168/168 [==============================] - 0s 77us/step - loss: 0.1172 - accuracy: 0.9881 - val_loss: 0.7901 - val_accuracy: 0.6667 Epoch 44/100 168/168 [==============================] - 0s 71us/step - loss: 0.1115 - accuracy: 0.9881 - val_loss: 0.8162 - val_accuracy: 0.6667 Epoch 45/100 168/168 [==============================] - 0s 71us/step - loss: 0.1054 - accuracy: 0.9821 - val_loss: 0.8401 - val_accuracy: 0.6842 Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 46/100 168/168 [==============================] - 0s 65us/step - loss: 0.0997 - accuracy: 0.9881 - val_loss: 0.8561 - val_accuracy: 0.6667 Epoch 47/100 168/168 [==============================] - 0s 71us/step - loss: 0.0961 - accuracy: 0.9881 - val_loss: 0.8576 - val_accuracy: 0.6667 Epoch 48/100 168/168 [==============================] - 0s 71us/step - loss: 0.0936 - accuracy: 0.9881 - val_loss: 0.8643 - val_accuracy: 0.6667 Epoch 49/100 168/168 [==============================] - 0s 71us/step - loss: 0.0908 - accuracy: 0.9940 - val_loss: 0.8631 - val_accuracy: 0.6491 Epoch 50/100 168/168 [==============================] - 0s 71us/step - loss: 0.0884 - accuracy: 0.9940 - val_loss: 0.8676 - val_accuracy: 0.6491 Epoch 51/100 168/168 [==============================] - 0s 71us/step - loss: 0.0856 - accuracy: 0.9940 - val_loss: 0.8607 - val_accuracy: 0.6491 Epoch 52/100 168/168 [==============================] - 0s 77us/step - loss: 0.0838 - accuracy: 0.9881 - val_loss: 0.8592 - val_accuracy: 0.6491 Epoch 53/100 168/168 [==============================] - 0s 71us/step - loss: 0.0819 - accuracy: 0.9881 - val_loss: 0.8771 - val_accuracy: 0.6667 Epoch 54/100 168/168 [==============================] - 0s 71us/step - loss: 0.0794 - accuracy: 0.9940 - val_loss: 0.8912 - val_accuracy: 0.6667 Epoch 55/100 168/168 [==============================] - 0s 65us/step - loss: 0.0777 - accuracy: 0.9940 - val_loss: 0.9022 - val_accuracy: 0.6667 Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 56/100 168/168 [==============================] - 0s 65us/step - loss: 0.0740 - accuracy: 1.0000 - val_loss: 0.8995 - val_accuracy: 0.6667 Epoch 57/100 168/168 [==============================] - 0s 71us/step - loss: 0.0733 - accuracy: 1.0000 - val_loss: 0.8922 - val_accuracy: 0.7018 Epoch 58/100 168/168 [==============================] - 0s 65us/step - loss: 0.0719 - accuracy: 1.0000 - val_loss: 0.8924 - val_accuracy: 0.6842 Epoch 59/100 168/168 [==============================] - 0s 65us/step - loss: 0.0707 - accuracy: 1.0000 - val_loss: 0.8931 - val_accuracy: 0.6842 Epoch 60/100 168/168 [==============================] - 0s 65us/step - loss: 0.0695 - accuracy: 1.0000 - val_loss: 0.8913 - val_accuracy: 0.6842 Epoch 61/100 168/168 [==============================] - 0s 77us/step - loss: 0.0685 - accuracy: 1.0000 - val_loss: 0.8970 - val_accuracy: 0.6842 Epoch 62/100 168/168 [==============================] - 0s 71us/step - loss: 0.0674 - accuracy: 1.0000 - val_loss: 0.8957 - val_accuracy: 0.7018 Epoch 63/100 168/168 [==============================] - 0s 71us/step - loss: 0.0663 - accuracy: 1.0000 - val_loss: 0.9018 - val_accuracy: 0.7018 Epoch 64/100 168/168 [==============================] - 0s 65us/step - loss: 0.0653 - accuracy: 1.0000 - val_loss: 0.9069 - val_accuracy: 0.6667 Epoch 65/100 168/168 [==============================] - 0s 83us/step - loss: 0.0646 - accuracy: 1.0000 - val_loss: 0.9088 - val_accuracy: 0.6842 Epoch 66/100 168/168 [==============================] - 0s 119us/step - loss: 0.0636 - accuracy: 1.0000 - val_loss: 0.9138 - val_accuracy: 0.6842 Epoch 67/100 168/168 [==============================] - 0s 101us/step - loss: 0.0626 - accuracy: 1.0000 - val_loss: 0.9116 - val_accuracy: 0.6842 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 68/100 168/168 [==============================] - 0s 65us/step - loss: 0.0616 - accuracy: 1.0000 - val_loss: 0.9120 - val_accuracy: 0.6842 Epoch 69/100 168/168 [==============================] - 0s 71us/step - loss: 0.0609 - accuracy: 1.0000 - val_loss: 0.9102 - val_accuracy: 0.6842 Epoch 70/100 168/168 [==============================] - 0s 71us/step - loss: 0.0605 - accuracy: 1.0000 - val_loss: 0.9081 - val_accuracy: 0.7018 Epoch 71/100 168/168 [==============================] - 0s 71us/step - loss: 0.0599 - accuracy: 1.0000 - val_loss: 0.9080 - val_accuracy: 0.7018 Epoch 72/100 168/168 [==============================] - 0s 65us/step - loss: 0.0596 - accuracy: 1.0000 - val_loss: 0.9082 - val_accuracy: 0.7018 Epoch 73/100 168/168 [==============================] - 0s 77us/step - loss: 0.0590 - accuracy: 1.0000 - val_loss: 0.9162 - val_accuracy: 0.7018 Epoch 74/100 168/168 [==============================] - 0s 71us/step - loss: 0.0586 - accuracy: 1.0000 - val_loss: 0.9215 - val_accuracy: 0.7018 Epoch 75/100 168/168 [==============================] - 0s 71us/step - loss: 0.0582 - accuracy: 1.0000 - val_loss: 0.9252 - val_accuracy: 0.7018 Epoch 76/100 168/168 [==============================] - 0s 71us/step - loss: 0.0577 - accuracy: 1.0000 - val_loss: 0.9273 - val_accuracy: 0.7018 Epoch 77/100 168/168 [==============================] - 0s 71us/step - loss: 0.0572 - accuracy: 1.0000 - val_loss: 0.9286 - val_accuracy: 0.7018 Epoch 00077: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 78/100 168/168 [==============================] - 0s 65us/step - loss: 0.0567 - accuracy: 1.0000 - val_loss: 0.9295 - val_accuracy: 0.7018 Epoch 79/100 168/168 [==============================] - 0s 77us/step - loss: 0.0566 - accuracy: 1.0000 - val_loss: 0.9291 - val_accuracy: 0.6842 Epoch 80/100 168/168 [==============================] - 0s 71us/step - loss: 0.0564 - accuracy: 1.0000 - val_loss: 0.9298 - val_accuracy: 0.6842 Epoch 81/100 168/168 [==============================] - 0s 71us/step - loss: 0.0561 - accuracy: 1.0000 - val_loss: 0.9305 - val_accuracy: 0.6842 Epoch 82/100 168/168 [==============================] - 0s 77us/step - loss: 0.0559 - accuracy: 1.0000 - val_loss: 0.9319 - val_accuracy: 0.6842 Epoch 83/100 168/168 [==============================] - 0s 71us/step - loss: 0.0557 - accuracy: 1.0000 - val_loss: 0.9349 - val_accuracy: 0.6842 Epoch 84/100 168/168 [==============================] - 0s 65us/step - loss: 0.0554 - accuracy: 1.0000 - val_loss: 0.9362 - val_accuracy: 0.6842 Epoch 85/100 168/168 [==============================] - 0s 65us/step - loss: 0.0553 - accuracy: 1.0000 - val_loss: 0.9369 - val_accuracy: 0.6842 Epoch 86/100 168/168 [==============================] - 0s 65us/step - loss: 0.0551 - accuracy: 1.0000 - val_loss: 0.9363 - val_accuracy: 0.6842 Epoch 87/100 168/168 [==============================] - 0s 65us/step - loss: 0.0549 - accuracy: 1.0000 - val_loss: 0.9361 - val_accuracy: 0.6842 Epoch 00087: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 88/100 168/168 [==============================] - 0s 65us/step - loss: 0.0546 - accuracy: 1.0000 - val_loss: 0.9357 - val_accuracy: 0.6842 Epoch 89/100 168/168 [==============================] - 0s 65us/step - loss: 0.0545 - accuracy: 1.0000 - val_loss: 0.9352 - val_accuracy: 0.6842 Epoch 90/100 168/168 [==============================] - 0s 65us/step - loss: 0.0544 - accuracy: 1.0000 - val_loss: 0.9354 - val_accuracy: 0.6842 Epoch 91/100 168/168 [==============================] - 0s 65us/step - loss: 0.0543 - accuracy: 1.0000 - val_loss: 0.9351 - val_accuracy: 0.6842 Epoch 92/100 168/168 [==============================] - 0s 77us/step - loss: 0.0542 - accuracy: 1.0000 - val_loss: 0.9347 - val_accuracy: 0.6842 Epoch 93/100 168/168 [==============================] - 0s 113us/step - loss: 0.0540 - accuracy: 1.0000 - val_loss: 0.9349 - val_accuracy: 0.6842 Epoch 94/100 168/168 [==============================] - 0s 107us/step - loss: 0.0540 - accuracy: 1.0000 - val_loss: 0.9350 - val_accuracy: 0.7018 Epoch 95/100 168/168 [==============================] - 0s 101us/step - loss: 0.0538 - accuracy: 1.0000 - val_loss: 0.9367 - val_accuracy: 0.7018 Epoch 96/100 168/168 [==============================] - 0s 89us/step - loss: 0.0537 - accuracy: 1.0000 - val_loss: 0.9379 - val_accuracy: 0.7018 Epoch 97/100 168/168 [==============================] - 0s 71us/step - loss: 0.0536 - accuracy: 1.0000 - val_loss: 0.9380 - val_accuracy: 0.7018 Epoch 00097: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 98/100 168/168 [==============================] - 0s 71us/step - loss: 0.0535 - accuracy: 1.0000 - val_loss: 0.9380 - val_accuracy: 0.7018 Epoch 99/100 168/168 [==============================] - 0s 71us/step - loss: 0.0534 - accuracy: 1.0000 - val_loss: 0.9380 - val_accuracy: 0.7018 Epoch 100/100 168/168 [==============================] - 0s 77us/step - loss: 0.0533 - accuracy: 1.0000 - val_loss: 0.9381 - val_accuracy: 0.7018
In [306]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [307]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
57/57 [==============================] - 0s 53us/step test loss: 0.9380739571755392, test accuracy: 0.7017543911933899
In [308]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7188365650969529
In [309]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.37037037037037035
KMeans¶
In [177]:
X
Out[177]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.430409 | -0.286045 | 0.490919 | 0.872836 | -0.908379 | -0.032724 | -0.630149 | -0.077256 | -0.666116 | -0.409507 | -0.808189 | -0.316827 | 1.001635 |
| 1 | -0.282157 | -1.478798 | -1.125065 | 1.134727 | 0.294401 | -1.552108 | -2.048764 | -0.332843 | -0.298952 | 1.114161 | 0.274322 | 1.796602 | 2.309045 |
| 2 | -0.419749 | 0.440468 | 0.853257 | 0.239250 | -0.398831 | -0.780274 | -0.360447 | -0.316940 | -0.976474 | 0.879549 | -0.457280 | 0.640345 | 0.645756 |
| 3 | -0.431942 | -1.517593 | 0.673149 | 0.786628 | -1.306695 | 1.366669 | 1.142564 | -2.034919 | -0.374780 | 0.475572 | -0.952521 | 0.198178 | 0.640593 |
| 4 | 0.011173 | 0.536562 | -0.966199 | -0.482951 | 0.331291 | 0.606478 | 0.539582 | -0.270964 | 0.042364 | -0.165555 | 0.123590 | 0.101357 | -0.658096 |
| 5 | 0.126251 | -0.590904 | -1.517639 | -1.072279 | 0.624234 | 1.023491 | 0.771080 | 0.598433 | -0.513511 | -0.860661 | 0.652573 | 1.694095 | 0.860230 |
| 6 | 0.830490 | -0.982750 | 2.985373 | 1.132038 | 0.506664 | 3.816969 | -0.181042 | -0.077794 | -0.404476 | -0.632689 | -0.730525 | -0.299915 | -0.619079 |
| 7 | -0.461917 | 0.208363 | 0.278132 | 2.301636 | 1.667266 | 0.690336 | 1.117026 | 1.428294 | 1.041434 | 0.404890 | -1.225161 | -1.083934 | 0.065409 |
| 8 | 0.249199 | -0.247143 | -0.916299 | 0.679460 | -0.342859 | 0.174455 | 0.125219 | 0.900110 | 0.761341 | -0.398986 | -1.063148 | 0.855251 | 2.099798 |
| 9 | 0.311144 | 1.364526 | -0.004701 | 0.405862 | -0.939024 | 0.796235 | -0.589807 | -0.084900 | 0.092344 | -0.628186 | -0.601718 | -0.966679 | 0.086483 |
| 10 | 1.334587 | 0.242352 | 0.597807 | 1.994512 | -0.050194 | 0.166573 | -0.234755 | 0.602868 | -1.535513 | -3.732625 | -0.901353 | 0.263896 | -1.060994 |
| 11 | 1.318614 | -0.797973 | 0.011175 | -0.735851 | -0.310309 | -1.472949 | -0.886459 | 0.062987 | -0.118121 | 0.096635 | 0.405075 | -1.082018 | 0.068160 |
| 12 | 0.824621 | 1.143049 | 0.195198 | -0.036161 | 0.107483 | -0.112574 | -0.229915 | -0.249936 | 0.727625 | -0.048902 | 0.611366 | 0.128240 | 0.758492 |
| 13 | -0.568472 | -0.848858 | 0.090767 | 1.154534 | -0.215999 | -2.659688 | -1.881352 | 0.412350 | -0.790718 | 0.827656 | 0.539638 | 1.593572 | 2.002635 |
| 14 | 0.406968 | 1.201176 | 1.075155 | -0.124700 | 0.135767 | -0.178145 | -0.204776 | -1.618374 | 0.193121 | 0.693318 | 1.055785 | -0.160648 | 0.537962 |
| 15 | -0.287723 | 1.515404 | 0.539186 | 0.427811 | -0.006824 | 0.845973 | 0.412855 | -0.313048 | 0.703395 | 0.284501 | -0.348641 | -0.519018 | -0.664237 |
| 16 | 0.522131 | 0.617054 | 0.277861 | -0.836147 | -1.629099 | -0.005864 | 0.191114 | 0.043306 | 0.248219 | 0.814197 | -0.101490 | -0.978501 | -0.398761 |
| 17 | -0.896889 | -1.308091 | -0.693222 | 0.501341 | -0.162478 | -0.037095 | -0.270506 | 0.346239 | 0.691093 | -0.015841 | -0.871109 | -1.169731 | 0.270587 |
| 18 | 0.875184 | 0.085665 | 0.080385 | 0.242890 | -0.938716 | -1.140718 | -0.097002 | -0.428159 | -0.490553 | -0.095430 | -0.233748 | -0.812311 | 0.895950 |
| 19 | -0.659140 | -0.549349 | -0.129098 | 1.107800 | -3.028026 | -0.963090 | 0.170241 | -2.196582 | -0.518279 | 1.312693 | -1.543068 | -0.398880 | 0.589793 |
| 20 | -0.563764 | -2.009854 | 0.074903 | -0.464555 | -1.109839 | 0.088831 | -0.566914 | 0.384785 | -0.625854 | -0.723645 | -1.000855 | 0.809858 | 0.131808 |
| 21 | 1.250269 | 0.010904 | 0.201698 | -0.957619 | -0.013951 | 1.101073 | -0.075927 | -0.502371 | -1.866004 | 0.114645 | 0.156305 | 0.629372 | 1.366339 |
| 22 | -1.190023 | -0.268956 | -0.555588 | -0.621566 | -0.230031 | 0.264870 | -0.241968 | 0.258810 | 0.329697 | 0.359684 | 0.486221 | 0.532413 | 0.397267 |
| 23 | -0.802365 | 0.152404 | -0.567015 | 0.381822 | 0.592009 | -0.562610 | -1.563042 | 0.721323 | 2.941459 | 2.101624 | 0.519023 | -0.366100 | -0.253206 |
| 24 | -0.893865 | 0.341962 | -0.830315 | 0.329795 | 0.563212 | -0.327526 | -1.488529 | 0.513889 | 2.829533 | 2.088829 | 0.636581 | -0.214855 | -0.258980 |
| 25 | -0.121562 | 1.347439 | 0.744281 | 0.698770 | -0.746534 | -0.657656 | -0.027263 | -0.447918 | 0.338675 | 1.028922 | 0.807479 | 0.808201 | -0.400533 |
| 26 | -1.615036 | 1.323285 | 0.625582 | 0.721012 | -0.691466 | -0.853918 | -0.638037 | -0.521595 | 0.602985 | 0.889454 | -0.304429 | -1.597203 | -0.132420 |
| 27 | -0.522333 | 1.534988 | -0.017521 | -0.056191 | 0.437400 | 1.304359 | 0.421225 | -0.229724 | 0.856944 | 0.363658 | -0.300702 | 0.199378 | -0.091915 |
| 28 | 0.020009 | -0.529617 | -0.687843 | -1.068170 | -0.317981 | -0.873679 | 2.545261 | 0.319549 | 0.389927 | -1.527578 | -0.515574 | -1.761353 | 0.997758 |
| 29 | 0.294921 | -0.175540 | -2.170589 | 0.206338 | 0.608432 | -2.666144 | -0.240215 | 1.450295 | 1.100392 | -1.316262 | 0.000504 | -3.910703 | -0.679615 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | -0.713121 | -0.124100 | 0.184524 | -0.289193 | 1.143155 | -0.040337 | -0.456140 | 0.386906 | -0.409479 | -0.013040 | -0.448614 | 0.678472 | 0.856538 |
| 196 | 0.088904 | -0.480222 | 0.314568 | 0.011773 | -0.871197 | -0.254444 | -1.214378 | 0.074859 | 0.805154 | 0.107568 | -0.101697 | 0.725634 | -0.674412 |
| 197 | -0.819978 | -0.214030 | 0.603520 | -0.224844 | 0.008606 | 0.221715 | 0.248298 | 0.580332 | 0.548519 | 1.036227 | 0.084133 | -0.973833 | -1.118049 |
| 198 | 0.089868 | -0.642488 | -0.163873 | 0.162352 | -2.220413 | -1.138568 | -0.479671 | 2.364939 | 0.560742 | -0.314032 | 1.123286 | 0.586946 | 1.028865 |
| 199 | 0.490718 | -0.570100 | -0.601226 | -1.373455 | -2.219961 | -1.917807 | -1.874507 | 2.571947 | 0.227763 | -1.470442 | 0.797456 | 0.390489 | 0.443397 |
| 200 | -0.725311 | 0.995058 | 1.698440 | -0.409174 | 1.345314 | -0.371509 | 0.853418 | 0.950027 | 0.524004 | -0.776995 | 0.384800 | 0.564620 | 0.242242 |
| 201 | -0.088956 | 0.381477 | 0.665527 | 0.871194 | 0.436489 | 0.159443 | 0.297415 | 0.889031 | -0.725496 | -0.079337 | 0.812380 | 0.888138 | 0.315317 |
| 202 | -0.627833 | 0.625737 | 0.574533 | 0.351258 | 0.679287 | 0.045521 | -0.005976 | -0.356564 | -0.077959 | -0.043215 | 1.410034 | 0.680198 | 0.118446 |
| 203 | -0.591412 | -1.691897 | -1.280443 | 0.004164 | 0.029231 | 1.050215 | 1.339125 | 0.772476 | -0.548882 | -0.875977 | -0.739724 | -0.002660 | 0.550573 |
| 204 | -0.213359 | -0.655305 | -0.545729 | -0.525488 | -0.788705 | -0.715509 | 0.006099 | 0.426857 | 0.380788 | 0.708127 | 1.161262 | 0.582157 | 0.085247 |
| 205 | 0.561065 | -0.270819 | -0.376934 | 1.362033 | -0.711445 | 1.354311 | -0.206743 | -0.142823 | 1.553667 | -0.536125 | -1.684840 | 0.688414 | -1.009051 |
| 206 | 0.905511 | 0.711971 | 1.427482 | 0.086910 | -0.261378 | 0.561744 | 0.222402 | -1.622424 | 0.024138 | 0.934010 | -0.468465 | 0.170884 | 0.221140 |
| 207 | 0.067531 | 0.386370 | -0.307384 | -0.040460 | -0.049760 | -0.032394 | 0.431908 | 1.042755 | -1.011654 | -0.412244 | -0.108096 | 0.184730 | -0.138904 |
| 208 | -0.059758 | 0.045608 | -0.194712 | -0.038341 | -0.248229 | 0.218675 | -0.402325 | 0.773505 | 0.041528 | 0.766170 | -0.042900 | 0.536066 | 0.856860 |
| 209 | 0.486749 | -0.756717 | -0.874808 | -0.729025 | -1.528664 | 0.066851 | -0.217409 | 2.535472 | 0.810467 | 2.007519 | -1.633543 | 1.078687 | -1.214695 |
| 210 | -0.009918 | 0.023157 | -0.108599 | -0.353482 | -0.525150 | 0.026241 | -0.209046 | 0.952549 | 0.118271 | 0.761473 | -0.021901 | 0.723007 | 1.176221 |
| 211 | -0.852239 | -0.126721 | 1.768756 | -0.139569 | 1.457419 | -1.896514 | 0.722738 | -1.858343 | 1.023542 | 0.337423 | -1.696471 | 0.797698 | -1.801833 |
| 212 | -1.605282 | 0.546705 | -0.027523 | -0.007901 | 0.390982 | 0.752113 | 0.108134 | -0.532402 | -0.658558 | -0.655673 | -0.110552 | -0.038507 | 0.564082 |
| 213 | -1.537486 | 0.438542 | -0.054954 | -0.009054 | 0.565426 | 0.944990 | 0.000999 | -0.699569 | -0.616522 | -0.546167 | 0.075944 | 0.000029 | 0.772172 |
| 214 | 0.478176 | -0.623588 | -1.163628 | -0.024044 | -0.377051 | 0.114672 | -1.189664 | -0.599743 | 0.064422 | -0.284247 | 0.793914 | 0.752339 | -0.558744 |
| 215 | -0.653553 | -0.272142 | 0.596156 | 0.881373 | -2.295187 | 0.283720 | -0.193981 | -0.067370 | 0.777762 | -0.959991 | -0.275185 | 0.990175 | -2.038870 |
| 216 | -0.746791 | -0.229040 | 0.929885 | 0.869993 | -2.913181 | 0.212781 | -0.305148 | -0.195613 | 1.117807 | -0.935236 | -0.012361 | 1.696083 | -2.096406 |
| 217 | -0.402132 | 0.567649 | 0.658617 | 1.252447 | 0.282722 | -0.867962 | -0.658417 | -0.711252 | 0.412558 | 0.018922 | -0.656841 | -0.668848 | -0.606249 |
| 218 | 0.253416 | 2.028743 | -0.249389 | -0.135717 | -0.432099 | -1.127803 | 0.330577 | -0.150249 | 1.400036 | -0.237307 | -1.036935 | 0.796314 | 0.315125 |
| 219 | -0.268737 | 0.304053 | 0.442309 | 1.466913 | -0.190859 | -1.339833 | -0.412332 | -0.934782 | -0.340795 | -0.803146 | -0.913412 | -1.601519 | -0.895268 |
| 220 | 0.164485 | 1.445490 | 2.674724 | -0.663649 | -0.232015 | 1.428702 | 0.961717 | -2.600552 | -0.356496 | 0.842619 | 2.796380 | 0.923058 | 1.962832 |
| 221 | 0.702551 | 0.697481 | 0.141117 | -0.647568 | 0.265119 | 0.543185 | 0.967290 | -0.204736 | -0.388782 | -1.297479 | 1.446403 | 1.404421 | 0.191658 |
| 222 | 0.727431 | 0.078441 | -0.027658 | -0.293281 | -0.116893 | 0.762821 | -0.474665 | -0.142512 | -0.107789 | 0.767739 | -0.106817 | -0.016677 | 0.681705 |
| 223 | -1.601068 | 0.456897 | -0.776221 | -0.202831 | 0.972321 | 1.554034 | 1.293388 | 0.533103 | -0.658778 | -0.011963 | -0.965611 | 1.297730 | 2.334936 |
| 224 | -0.775289 | -1.780714 | -0.773207 | -0.130797 | -0.258296 | 0.465109 | 0.964189 | -0.054493 | -0.318554 | -0.116591 | 0.490944 | 0.263715 | 0.162778 |
225 rows × 13 columns
In [178]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[178]:
[2925.0, 2654.4694551642833, 2487.537140339455, 2347.3385606542174, 2241.219607532395, 2187.984558812649, 2078.2651666536785, 1992.3748205985125, 1984.6213758642439, 1904.4598519281963, 1850.0132981301954, 1816.2106987200941, 1761.4652710138919, 1722.4326028221699]
In [179]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[179]:
[<matplotlib.lines.Line2D at 0x1e82f5d1048>]
K=2
In [180]:
kmeans_mfcc = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_mfcc.fit(X)
Out[180]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [181]:
kmeans_mfcc.labels_
Out[181]:
array([1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 0])
In [182]:
clusters_mfcc = kmeans_mfcc.predict(X)
clusters_mfcc
Out[182]:
array([1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 0])
In [183]:
X.loc[:,'Cluster'] = clusters_mfcc
X.loc[:,'chosen'] = list(y)
In [184]:
X
Out[184]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.430409 | -0.286045 | 0.490919 | 0.872836 | -0.908379 | -0.032724 | -0.630149 | -0.077256 | -0.666116 | -0.409507 | -0.808189 | -0.316827 | 1.001635 | 1 | 0 |
| 1 | -0.282157 | -1.478798 | -1.125065 | 1.134727 | 0.294401 | -1.552108 | -2.048764 | -0.332843 | -0.298952 | 1.114161 | 0.274322 | 1.796602 | 2.309045 | 1 | 0 |
| 2 | -0.419749 | 0.440468 | 0.853257 | 0.239250 | -0.398831 | -0.780274 | -0.360447 | -0.316940 | -0.976474 | 0.879549 | -0.457280 | 0.640345 | 0.645756 | 1 | 0 |
| 3 | -0.431942 | -1.517593 | 0.673149 | 0.786628 | -1.306695 | 1.366669 | 1.142564 | -2.034919 | -0.374780 | 0.475572 | -0.952521 | 0.198178 | 0.640593 | 1 | 0 |
| 4 | 0.011173 | 0.536562 | -0.966199 | -0.482951 | 0.331291 | 0.606478 | 0.539582 | -0.270964 | 0.042364 | -0.165555 | 0.123590 | 0.101357 | -0.658096 | 0 | 0 |
| 5 | 0.126251 | -0.590904 | -1.517639 | -1.072279 | 0.624234 | 1.023491 | 0.771080 | 0.598433 | -0.513511 | -0.860661 | 0.652573 | 1.694095 | 0.860230 | 0 | 0 |
| 6 | 0.830490 | -0.982750 | 2.985373 | 1.132038 | 0.506664 | 3.816969 | -0.181042 | -0.077794 | -0.404476 | -0.632689 | -0.730525 | -0.299915 | -0.619079 | 1 | 0 |
| 7 | -0.461917 | 0.208363 | 0.278132 | 2.301636 | 1.667266 | 0.690336 | 1.117026 | 1.428294 | 1.041434 | 0.404890 | -1.225161 | -1.083934 | 0.065409 | 1 | 0 |
| 8 | 0.249199 | -0.247143 | -0.916299 | 0.679460 | -0.342859 | 0.174455 | 0.125219 | 0.900110 | 0.761341 | -0.398986 | -1.063148 | 0.855251 | 2.099798 | 1 | 0 |
| 9 | 0.311144 | 1.364526 | -0.004701 | 0.405862 | -0.939024 | 0.796235 | -0.589807 | -0.084900 | 0.092344 | -0.628186 | -0.601718 | -0.966679 | 0.086483 | 1 | 0 |
| 10 | 1.334587 | 0.242352 | 0.597807 | 1.994512 | -0.050194 | 0.166573 | -0.234755 | 0.602868 | -1.535513 | -3.732625 | -0.901353 | 0.263896 | -1.060994 | 1 | 0 |
| 11 | 1.318614 | -0.797973 | 0.011175 | -0.735851 | -0.310309 | -1.472949 | -0.886459 | 0.062987 | -0.118121 | 0.096635 | 0.405075 | -1.082018 | 0.068160 | 0 | 0 |
| 12 | 0.824621 | 1.143049 | 0.195198 | -0.036161 | 0.107483 | -0.112574 | -0.229915 | -0.249936 | 0.727625 | -0.048902 | 0.611366 | 0.128240 | 0.758492 | 1 | 0 |
| 13 | -0.568472 | -0.848858 | 0.090767 | 1.154534 | -0.215999 | -2.659688 | -1.881352 | 0.412350 | -0.790718 | 0.827656 | 0.539638 | 1.593572 | 2.002635 | 1 | 0 |
| 14 | 0.406968 | 1.201176 | 1.075155 | -0.124700 | 0.135767 | -0.178145 | -0.204776 | -1.618374 | 0.193121 | 0.693318 | 1.055785 | -0.160648 | 0.537962 | 1 | 0 |
| 15 | -0.287723 | 1.515404 | 0.539186 | 0.427811 | -0.006824 | 0.845973 | 0.412855 | -0.313048 | 0.703395 | 0.284501 | -0.348641 | -0.519018 | -0.664237 | 1 | 0 |
| 16 | 0.522131 | 0.617054 | 0.277861 | -0.836147 | -1.629099 | -0.005864 | 0.191114 | 0.043306 | 0.248219 | 0.814197 | -0.101490 | -0.978501 | -0.398761 | 0 | 0 |
| 17 | -0.896889 | -1.308091 | -0.693222 | 0.501341 | -0.162478 | -0.037095 | -0.270506 | 0.346239 | 0.691093 | -0.015841 | -0.871109 | -1.169731 | 0.270587 | 0 | 0 |
| 18 | 0.875184 | 0.085665 | 0.080385 | 0.242890 | -0.938716 | -1.140718 | -0.097002 | -0.428159 | -0.490553 | -0.095430 | -0.233748 | -0.812311 | 0.895950 | 1 | 0 |
| 19 | -0.659140 | -0.549349 | -0.129098 | 1.107800 | -3.028026 | -0.963090 | 0.170241 | -2.196582 | -0.518279 | 1.312693 | -1.543068 | -0.398880 | 0.589793 | 1 | 0 |
| 20 | -0.563764 | -2.009854 | 0.074903 | -0.464555 | -1.109839 | 0.088831 | -0.566914 | 0.384785 | -0.625854 | -0.723645 | -1.000855 | 0.809858 | 0.131808 | 0 | 0 |
| 21 | 1.250269 | 0.010904 | 0.201698 | -0.957619 | -0.013951 | 1.101073 | -0.075927 | -0.502371 | -1.866004 | 0.114645 | 0.156305 | 0.629372 | 1.366339 | 1 | 0 |
| 22 | -1.190023 | -0.268956 | -0.555588 | -0.621566 | -0.230031 | 0.264870 | -0.241968 | 0.258810 | 0.329697 | 0.359684 | 0.486221 | 0.532413 | 0.397267 | 0 | 0 |
| 23 | -0.802365 | 0.152404 | -0.567015 | 0.381822 | 0.592009 | -0.562610 | -1.563042 | 0.721323 | 2.941459 | 2.101624 | 0.519023 | -0.366100 | -0.253206 | 0 | 0 |
| 24 | -0.893865 | 0.341962 | -0.830315 | 0.329795 | 0.563212 | -0.327526 | -1.488529 | 0.513889 | 2.829533 | 2.088829 | 0.636581 | -0.214855 | -0.258980 | 0 | 0 |
| 25 | -0.121562 | 1.347439 | 0.744281 | 0.698770 | -0.746534 | -0.657656 | -0.027263 | -0.447918 | 0.338675 | 1.028922 | 0.807479 | 0.808201 | -0.400533 | 1 | 0 |
| 26 | -1.615036 | 1.323285 | 0.625582 | 0.721012 | -0.691466 | -0.853918 | -0.638037 | -0.521595 | 0.602985 | 0.889454 | -0.304429 | -1.597203 | -0.132420 | 1 | 0 |
| 27 | -0.522333 | 1.534988 | -0.017521 | -0.056191 | 0.437400 | 1.304359 | 0.421225 | -0.229724 | 0.856944 | 0.363658 | -0.300702 | 0.199378 | -0.091915 | 1 | 0 |
| 28 | 0.020009 | -0.529617 | -0.687843 | -1.068170 | -0.317981 | -0.873679 | 2.545261 | 0.319549 | 0.389927 | -1.527578 | -0.515574 | -1.761353 | 0.997758 | 0 | 0 |
| 29 | 0.294921 | -0.175540 | -2.170589 | 0.206338 | 0.608432 | -2.666144 | -0.240215 | 1.450295 | 1.100392 | -1.316262 | 0.000504 | -3.910703 | -0.679615 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | -0.713121 | -0.124100 | 0.184524 | -0.289193 | 1.143155 | -0.040337 | -0.456140 | 0.386906 | -0.409479 | -0.013040 | -0.448614 | 0.678472 | 0.856538 | 1 | 1 |
| 196 | 0.088904 | -0.480222 | 0.314568 | 0.011773 | -0.871197 | -0.254444 | -1.214378 | 0.074859 | 0.805154 | 0.107568 | -0.101697 | 0.725634 | -0.674412 | 0 | 1 |
| 197 | -0.819978 | -0.214030 | 0.603520 | -0.224844 | 0.008606 | 0.221715 | 0.248298 | 0.580332 | 0.548519 | 1.036227 | 0.084133 | -0.973833 | -1.118049 | 0 | 1 |
| 198 | 0.089868 | -0.642488 | -0.163873 | 0.162352 | -2.220413 | -1.138568 | -0.479671 | 2.364939 | 0.560742 | -0.314032 | 1.123286 | 0.586946 | 1.028865 | 0 | 1 |
| 199 | 0.490718 | -0.570100 | -0.601226 | -1.373455 | -2.219961 | -1.917807 | -1.874507 | 2.571947 | 0.227763 | -1.470442 | 0.797456 | 0.390489 | 0.443397 | 0 | 1 |
| 200 | -0.725311 | 0.995058 | 1.698440 | -0.409174 | 1.345314 | -0.371509 | 0.853418 | 0.950027 | 0.524004 | -0.776995 | 0.384800 | 0.564620 | 0.242242 | 1 | 1 |
| 201 | -0.088956 | 0.381477 | 0.665527 | 0.871194 | 0.436489 | 0.159443 | 0.297415 | 0.889031 | -0.725496 | -0.079337 | 0.812380 | 0.888138 | 0.315317 | 1 | 1 |
| 202 | -0.627833 | 0.625737 | 0.574533 | 0.351258 | 0.679287 | 0.045521 | -0.005976 | -0.356564 | -0.077959 | -0.043215 | 1.410034 | 0.680198 | 0.118446 | 1 | 1 |
| 203 | -0.591412 | -1.691897 | -1.280443 | 0.004164 | 0.029231 | 1.050215 | 1.339125 | 0.772476 | -0.548882 | -0.875977 | -0.739724 | -0.002660 | 0.550573 | 0 | 1 |
| 204 | -0.213359 | -0.655305 | -0.545729 | -0.525488 | -0.788705 | -0.715509 | 0.006099 | 0.426857 | 0.380788 | 0.708127 | 1.161262 | 0.582157 | 0.085247 | 0 | 1 |
| 205 | 0.561065 | -0.270819 | -0.376934 | 1.362033 | -0.711445 | 1.354311 | -0.206743 | -0.142823 | 1.553667 | -0.536125 | -1.684840 | 0.688414 | -1.009051 | 0 | 1 |
| 206 | 0.905511 | 0.711971 | 1.427482 | 0.086910 | -0.261378 | 0.561744 | 0.222402 | -1.622424 | 0.024138 | 0.934010 | -0.468465 | 0.170884 | 0.221140 | 1 | 1 |
| 207 | 0.067531 | 0.386370 | -0.307384 | -0.040460 | -0.049760 | -0.032394 | 0.431908 | 1.042755 | -1.011654 | -0.412244 | -0.108096 | 0.184730 | -0.138904 | 0 | 1 |
| 208 | -0.059758 | 0.045608 | -0.194712 | -0.038341 | -0.248229 | 0.218675 | -0.402325 | 0.773505 | 0.041528 | 0.766170 | -0.042900 | 0.536066 | 0.856860 | 1 | 1 |
| 209 | 0.486749 | -0.756717 | -0.874808 | -0.729025 | -1.528664 | 0.066851 | -0.217409 | 2.535472 | 0.810467 | 2.007519 | -1.633543 | 1.078687 | -1.214695 | 0 | 1 |
| 210 | -0.009918 | 0.023157 | -0.108599 | -0.353482 | -0.525150 | 0.026241 | -0.209046 | 0.952549 | 0.118271 | 0.761473 | -0.021901 | 0.723007 | 1.176221 | 1 | 1 |
| 211 | -0.852239 | -0.126721 | 1.768756 | -0.139569 | 1.457419 | -1.896514 | 0.722738 | -1.858343 | 1.023542 | 0.337423 | -1.696471 | 0.797698 | -1.801833 | 1 | 1 |
| 212 | -1.605282 | 0.546705 | -0.027523 | -0.007901 | 0.390982 | 0.752113 | 0.108134 | -0.532402 | -0.658558 | -0.655673 | -0.110552 | -0.038507 | 0.564082 | 1 | 1 |
| 213 | -1.537486 | 0.438542 | -0.054954 | -0.009054 | 0.565426 | 0.944990 | 0.000999 | -0.699569 | -0.616522 | -0.546167 | 0.075944 | 0.000029 | 0.772172 | 1 | 1 |
| 214 | 0.478176 | -0.623588 | -1.163628 | -0.024044 | -0.377051 | 0.114672 | -1.189664 | -0.599743 | 0.064422 | -0.284247 | 0.793914 | 0.752339 | -0.558744 | 0 | 1 |
| 215 | -0.653553 | -0.272142 | 0.596156 | 0.881373 | -2.295187 | 0.283720 | -0.193981 | -0.067370 | 0.777762 | -0.959991 | -0.275185 | 0.990175 | -2.038870 | 0 | 1 |
| 216 | -0.746791 | -0.229040 | 0.929885 | 0.869993 | -2.913181 | 0.212781 | -0.305148 | -0.195613 | 1.117807 | -0.935236 | -0.012361 | 1.696083 | -2.096406 | 0 | 1 |
| 217 | -0.402132 | 0.567649 | 0.658617 | 1.252447 | 0.282722 | -0.867962 | -0.658417 | -0.711252 | 0.412558 | 0.018922 | -0.656841 | -0.668848 | -0.606249 | 1 | 1 |
| 218 | 0.253416 | 2.028743 | -0.249389 | -0.135717 | -0.432099 | -1.127803 | 0.330577 | -0.150249 | 1.400036 | -0.237307 | -1.036935 | 0.796314 | 0.315125 | 1 | 1 |
| 219 | -0.268737 | 0.304053 | 0.442309 | 1.466913 | -0.190859 | -1.339833 | -0.412332 | -0.934782 | -0.340795 | -0.803146 | -0.913412 | -1.601519 | -0.895268 | 1 | 1 |
| 220 | 0.164485 | 1.445490 | 2.674724 | -0.663649 | -0.232015 | 1.428702 | 0.961717 | -2.600552 | -0.356496 | 0.842619 | 2.796380 | 0.923058 | 1.962832 | 1 | 1 |
| 221 | 0.702551 | 0.697481 | 0.141117 | -0.647568 | 0.265119 | 0.543185 | 0.967290 | -0.204736 | -0.388782 | -1.297479 | 1.446403 | 1.404421 | 0.191658 | 1 | 1 |
| 222 | 0.727431 | 0.078441 | -0.027658 | -0.293281 | -0.116893 | 0.762821 | -0.474665 | -0.142512 | -0.107789 | 0.767739 | -0.106817 | -0.016677 | 0.681705 | 1 | 1 |
| 223 | -1.601068 | 0.456897 | -0.776221 | -0.202831 | 0.972321 | 1.554034 | 1.293388 | 0.533103 | -0.658778 | -0.011963 | -0.965611 | 1.297730 | 2.334936 | 1 | 1 |
| 224 | -0.775289 | -1.780714 | -0.773207 | -0.130797 | -0.258296 | 0.465109 | 0.964189 | -0.054493 | -0.318554 | -0.116591 | 0.490944 | 0.263715 | 0.162778 | 0 | 1 |
225 rows × 15 columns
In [185]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[185]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82f5fadd8>
Tonal Centroid¶
In [310]:
df_n_ps_std[0].columns
Out[310]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
In [311]:
df_n_ps_std[0].columns[34:40]
Out[311]:
Index(['tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6'],
dtype='object')
In [312]:
df_n_ps_std_tc = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_tc[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,34:40])
df_n_ps_std_tc[i].columns=df_n_ps_std[i].columns[34:40]
df_n_ps_std_tc[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 372 entries, 0 to 371 Data columns (total 6 columns): tonalcentroidfiles_1 372 non-null float64 tonalcentroidfiles_2 372 non-null float64 tonalcentroidfiles_3 372 non-null float64 tonalcentroidfiles_4 372 non-null float64 tonalcentroidfiles_5 372 non-null float64 tonalcentroidfiles_6 372 non-null float64 dtypes: float64(6) memory usage: 17.5 KB
Arte Francés¶
ANN¶
In [313]:
X = df_n_ps_std_tc[0]
In [314]:
y = df_n_ps[0]['chosen']
In [315]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [316]:
X_train.shape
Out[316]:
(279, 6)
In [193]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [194]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [195]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [196]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.02, 'max_iter': 2000}, que permiten obtener un Accuracy de 78.85% y un Kappa del 15.23
Tiempo total: 15.53 minutos
In [317]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30,30,30]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.02
epochs = 2000
In [318]:
input_tensor = Input(shape = (n0,))
In [319]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [320]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [321]:
model.summary()
Model: "model_15" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_15 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_49 (Dense) (None, 30) 210 _________________________________________________________________ dense_50 (Dense) (None, 30) 930 _________________________________________________________________ dense_51 (Dense) (None, 30) 930 _________________________________________________________________ dense_52 (Dense) (None, 1) 31 ================================================================= Total params: 2,101 Trainable params: 2,101 Non-trainable params: 0 _________________________________________________________________
In [322]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 279 samples, validate on 93 samples Epoch 1/2000 279/279 [==============================] - 0s 795us/step - loss: 0.6163 - accuracy: 0.6810 - val_loss: 0.6134 - val_accuracy: 0.7312 Epoch 2/2000 279/279 [==============================] - 0s 57us/step - loss: 0.5435 - accuracy: 0.7778 - val_loss: 0.6137 - val_accuracy: 0.7204 Epoch 3/2000 279/279 [==============================] - 0s 54us/step - loss: 0.5501 - accuracy: 0.7742 - val_loss: 0.6025 - val_accuracy: 0.7312 Epoch 4/2000 279/279 [==============================] - 0s 61us/step - loss: 0.5280 - accuracy: 0.7814 - val_loss: 0.5825 - val_accuracy: 0.7419 Epoch 5/2000 279/279 [==============================] - 0s 54us/step - loss: 0.5163 - accuracy: 0.7670 - val_loss: 0.5768 - val_accuracy: 0.7312 Epoch 6/2000 279/279 [==============================] - 0s 57us/step - loss: 0.4872 - accuracy: 0.7849 - val_loss: 0.5917 - val_accuracy: 0.7312 Epoch 7/2000 279/279 [==============================] - 0s 75us/step - loss: 0.4670 - accuracy: 0.7957 - val_loss: 0.5862 - val_accuracy: 0.7204 Epoch 8/2000 279/279 [==============================] - 0s 72us/step - loss: 0.4556 - accuracy: 0.7993 - val_loss: 0.6149 - val_accuracy: 0.7204 Epoch 9/2000 279/279 [==============================] - 0s 68us/step - loss: 0.4441 - accuracy: 0.8100 - val_loss: 0.5995 - val_accuracy: 0.7204 Epoch 10/2000 279/279 [==============================] - 0s 64us/step - loss: 0.4241 - accuracy: 0.8315 - val_loss: 0.6087 - val_accuracy: 0.6882 Epoch 11/2000 279/279 [==============================] - 0s 57us/step - loss: 0.4078 - accuracy: 0.8136 - val_loss: 0.6182 - val_accuracy: 0.6989 Epoch 12/2000 279/279 [==============================] - 0s 57us/step - loss: 0.3746 - accuracy: 0.8387 - val_loss: 0.6277 - val_accuracy: 0.6989 Epoch 13/2000 279/279 [==============================] - 0s 57us/step - loss: 0.3551 - accuracy: 0.8710 - val_loss: 0.7196 - val_accuracy: 0.7204 Epoch 14/2000 279/279 [==============================] - 0s 57us/step - loss: 0.3640 - accuracy: 0.8530 - val_loss: 0.6662 - val_accuracy: 0.6774 Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.009999999776482582. Epoch 15/2000 279/279 [==============================] - 0s 57us/step - loss: 0.2963 - accuracy: 0.8781 - val_loss: 0.6874 - val_accuracy: 0.6989 Epoch 16/2000 279/279 [==============================] - 0s 82us/step - loss: 0.2739 - accuracy: 0.8781 - val_loss: 0.7054 - val_accuracy: 0.6882 Epoch 17/2000 279/279 [==============================] - 0s 72us/step - loss: 0.2490 - accuracy: 0.9176 - val_loss: 0.6976 - val_accuracy: 0.6989 Epoch 18/2000 279/279 [==============================] - 0s 72us/step - loss: 0.2295 - accuracy: 0.9032 - val_loss: 0.7744 - val_accuracy: 0.6882 Epoch 19/2000 279/279 [==============================] - 0s 61us/step - loss: 0.2130 - accuracy: 0.9283 - val_loss: 0.7568 - val_accuracy: 0.6667 Epoch 20/2000 279/279 [==============================] - 0s 64us/step - loss: 0.1941 - accuracy: 0.9427 - val_loss: 0.8372 - val_accuracy: 0.6774 Epoch 21/2000 279/279 [==============================] - 0s 61us/step - loss: 0.1811 - accuracy: 0.9319 - val_loss: 0.8294 - val_accuracy: 0.6452 Epoch 22/2000 279/279 [==============================] - 0s 64us/step - loss: 0.1768 - accuracy: 0.9462 - val_loss: 0.9309 - val_accuracy: 0.6774 Epoch 23/2000 279/279 [==============================] - 0s 82us/step - loss: 0.1713 - accuracy: 0.9427 - val_loss: 0.8720 - val_accuracy: 0.6774 Epoch 24/2000 279/279 [==============================] - 0s 61us/step - loss: 0.1431 - accuracy: 0.9462 - val_loss: 0.9373 - val_accuracy: 0.6667 Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.004999999888241291. Epoch 25/2000 279/279 [==============================] - 0s 57us/step - loss: 0.1293 - accuracy: 0.9677 - val_loss: 0.9036 - val_accuracy: 0.6559 Epoch 26/2000 279/279 [==============================] - 0s 61us/step - loss: 0.1134 - accuracy: 0.9677 - val_loss: 0.9900 - val_accuracy: 0.6882 Epoch 27/2000 279/279 [==============================] - 0s 57us/step - loss: 0.1019 - accuracy: 0.9677 - val_loss: 0.9436 - val_accuracy: 0.6667 Epoch 28/2000 279/279 [==============================] - 0s 72us/step - loss: 0.1001 - accuracy: 0.9749 - val_loss: 0.9939 - val_accuracy: 0.6774 Epoch 29/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0948 - accuracy: 0.9677 - val_loss: 1.0398 - val_accuracy: 0.6989 Epoch 30/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0832 - accuracy: 0.9713 - val_loss: 1.0118 - val_accuracy: 0.6667 Epoch 31/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0772 - accuracy: 0.9821 - val_loss: 1.0549 - val_accuracy: 0.6882 Epoch 32/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0739 - accuracy: 0.9785 - val_loss: 1.0726 - val_accuracy: 0.6667 Epoch 33/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0687 - accuracy: 0.9892 - val_loss: 1.1151 - val_accuracy: 0.6774 Epoch 34/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0661 - accuracy: 0.9821 - val_loss: 1.1437 - val_accuracy: 0.6882 Epoch 00034: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455. Epoch 35/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0571 - accuracy: 0.9892 - val_loss: 1.1363 - val_accuracy: 0.6882 Epoch 36/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0568 - accuracy: 0.9928 - val_loss: 1.1436 - val_accuracy: 0.6989 Epoch 37/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0530 - accuracy: 0.9928 - val_loss: 1.1831 - val_accuracy: 0.7097 Epoch 38/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0504 - accuracy: 0.9892 - val_loss: 1.1861 - val_accuracy: 0.6882 Epoch 39/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0480 - accuracy: 0.9964 - val_loss: 1.2017 - val_accuracy: 0.6882 Epoch 40/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0463 - accuracy: 0.9964 - val_loss: 1.2189 - val_accuracy: 0.6989 Epoch 41/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0442 - accuracy: 0.9964 - val_loss: 1.2194 - val_accuracy: 0.6882 Epoch 42/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0428 - accuracy: 0.9964 - val_loss: 1.2455 - val_accuracy: 0.6989 Epoch 43/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0403 - accuracy: 0.9964 - val_loss: 1.2560 - val_accuracy: 0.6882 Epoch 44/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0390 - accuracy: 0.9964 - val_loss: 1.2598 - val_accuracy: 0.6774 Epoch 00044: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228. Epoch 45/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0362 - accuracy: 0.9964 - val_loss: 1.2756 - val_accuracy: 0.6989 Epoch 46/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0356 - accuracy: 0.9964 - val_loss: 1.2850 - val_accuracy: 0.6989 Epoch 47/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0347 - accuracy: 0.9964 - val_loss: 1.2884 - val_accuracy: 0.6989 Epoch 48/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0338 - accuracy: 0.9964 - val_loss: 1.2949 - val_accuracy: 0.6989 Epoch 49/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0331 - accuracy: 0.9964 - val_loss: 1.3005 - val_accuracy: 0.6989 Epoch 50/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0321 - accuracy: 1.0000 - val_loss: 1.3091 - val_accuracy: 0.6989 Epoch 51/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0313 - accuracy: 1.0000 - val_loss: 1.3175 - val_accuracy: 0.6989 Epoch 52/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0308 - accuracy: 1.0000 - val_loss: 1.3265 - val_accuracy: 0.6989 Epoch 53/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0304 - accuracy: 1.0000 - val_loss: 1.3245 - val_accuracy: 0.6989 Epoch 54/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0295 - accuracy: 1.0000 - val_loss: 1.3377 - val_accuracy: 0.6989 Epoch 00054: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614. Epoch 55/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0286 - accuracy: 1.0000 - val_loss: 1.3390 - val_accuracy: 0.6989 Epoch 56/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0283 - accuracy: 1.0000 - val_loss: 1.3404 - val_accuracy: 0.6989 Epoch 57/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0279 - accuracy: 1.0000 - val_loss: 1.3436 - val_accuracy: 0.6989 Epoch 58/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0276 - accuracy: 1.0000 - val_loss: 1.3453 - val_accuracy: 0.6989 Epoch 59/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 1.3486 - val_accuracy: 0.6989 Epoch 60/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0268 - accuracy: 1.0000 - val_loss: 1.3547 - val_accuracy: 0.6989 Epoch 61/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0266 - accuracy: 1.0000 - val_loss: 1.3581 - val_accuracy: 0.6989 Epoch 62/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0263 - accuracy: 1.0000 - val_loss: 1.3635 - val_accuracy: 0.6989 Epoch 63/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0261 - accuracy: 1.0000 - val_loss: 1.3673 - val_accuracy: 0.7097 Epoch 64/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0257 - accuracy: 1.0000 - val_loss: 1.3713 - val_accuracy: 0.7097 Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807. Epoch 65/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0253 - accuracy: 1.0000 - val_loss: 1.3727 - val_accuracy: 0.7097 Epoch 66/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0252 - accuracy: 1.0000 - val_loss: 1.3727 - val_accuracy: 0.6989 Epoch 67/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0251 - accuracy: 1.0000 - val_loss: 1.3723 - val_accuracy: 0.6989 Epoch 68/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0249 - accuracy: 1.0000 - val_loss: 1.3740 - val_accuracy: 0.6989 Epoch 69/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0247 - accuracy: 1.0000 - val_loss: 1.3777 - val_accuracy: 0.6989 Epoch 70/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0246 - accuracy: 1.0000 - val_loss: 1.3802 - val_accuracy: 0.6989 Epoch 71/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0244 - accuracy: 1.0000 - val_loss: 1.3823 - val_accuracy: 0.6989 Epoch 72/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 1.3839 - val_accuracy: 0.6989 Epoch 73/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0241 - accuracy: 1.0000 - val_loss: 1.3851 - val_accuracy: 0.6989 Epoch 74/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0240 - accuracy: 1.0000 - val_loss: 1.3854 - val_accuracy: 0.6989 Epoch 00074: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035. Epoch 75/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0238 - accuracy: 1.0000 - val_loss: 1.3870 - val_accuracy: 0.6989 Epoch 76/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 1.3878 - val_accuracy: 0.6989 Epoch 77/2000 279/279 [==============================] - ETA: 0s - loss: 0.0402 - accuracy: 1.00 - 0s 57us/step - loss: 0.0236 - accuracy: 1.0000 - val_loss: 1.3888 - val_accuracy: 0.6989 Epoch 78/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0236 - accuracy: 1.0000 - val_loss: 1.3901 - val_accuracy: 0.6989 Epoch 79/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0235 - accuracy: 1.0000 - val_loss: 1.3912 - val_accuracy: 0.6989 Epoch 80/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0234 - accuracy: 1.0000 - val_loss: 1.3928 - val_accuracy: 0.6989 Epoch 81/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0233 - accuracy: 1.0000 - val_loss: 1.3938 - val_accuracy: 0.6989 Epoch 82/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0233 - accuracy: 1.0000 - val_loss: 1.3943 - val_accuracy: 0.6989 Epoch 83/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0232 - accuracy: 1.0000 - val_loss: 1.3947 - val_accuracy: 0.6989 Epoch 84/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0232 - accuracy: 1.0000 - val_loss: 1.3944 - val_accuracy: 0.6989 Epoch 00084: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05. Epoch 85/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0230 - accuracy: 1.0000 - val_loss: 1.3949 - val_accuracy: 0.6989 Epoch 86/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0230 - accuracy: 1.0000 - val_loss: 1.3958 - val_accuracy: 0.6989 Epoch 87/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 1.3960 - val_accuracy: 0.6989 Epoch 88/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 1.3967 - val_accuracy: 0.6989 Epoch 89/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0229 - accuracy: 1.0000 - val_loss: 1.3975 - val_accuracy: 0.6989 Epoch 90/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 1.3982 - val_accuracy: 0.6989 Epoch 91/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 1.3990 - val_accuracy: 0.6989 Epoch 92/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 1.3999 - val_accuracy: 0.6989 Epoch 93/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 1.4000 - val_accuracy: 0.6989 Epoch 94/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 1.4007 - val_accuracy: 0.6989 Epoch 00094: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05. Epoch 95/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 1.4009 - val_accuracy: 0.6989 Epoch 96/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 1.4010 - val_accuracy: 0.6989 Epoch 97/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 1.4014 - val_accuracy: 0.6989 Epoch 98/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 1.4017 - val_accuracy: 0.6989 Epoch 99/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 1.4018 - val_accuracy: 0.6989 Epoch 100/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 1.4022 - val_accuracy: 0.6989 Epoch 101/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 1.4023 - val_accuracy: 0.6989 Epoch 102/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 1.4027 - val_accuracy: 0.6989 Epoch 103/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 1.4029 - val_accuracy: 0.6989 Epoch 104/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4029 - val_accuracy: 0.6989 Epoch 00104: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05. Epoch 105/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4032 - val_accuracy: 0.6989 Epoch 106/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4033 - val_accuracy: 0.6989 Epoch 107/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4035 - val_accuracy: 0.6989 Epoch 108/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4036 - val_accuracy: 0.6989 Epoch 109/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4038 - val_accuracy: 0.6989 Epoch 110/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4038 - val_accuracy: 0.6989 Epoch 111/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4039 - val_accuracy: 0.6989 Epoch 112/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.4041 - val_accuracy: 0.6989 Epoch 113/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4042 - val_accuracy: 0.6989 Epoch 114/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4043 - val_accuracy: 0.6989 Epoch 00114: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06. Epoch 115/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4044 - val_accuracy: 0.6989 Epoch 116/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4045 - val_accuracy: 0.6989 Epoch 117/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4045 - val_accuracy: 0.6989 Epoch 118/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4046 - val_accuracy: 0.6989 Epoch 119/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4047 - val_accuracy: 0.6989 Epoch 120/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4048 - val_accuracy: 0.6989 Epoch 121/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4048 - val_accuracy: 0.6989 Epoch 122/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4049 - val_accuracy: 0.6989 Epoch 123/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4049 - val_accuracy: 0.6989 Epoch 124/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4050 - val_accuracy: 0.6989 Epoch 00124: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06. Epoch 125/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4050 - val_accuracy: 0.6989 Epoch 126/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4051 - val_accuracy: 0.6989 Epoch 127/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4051 - val_accuracy: 0.6989 Epoch 128/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4052 - val_accuracy: 0.6989 Epoch 129/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4052 - val_accuracy: 0.6989 Epoch 130/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4052 - val_accuracy: 0.6989 Epoch 131/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.4053 - val_accuracy: 0.6989 Epoch 132/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4053 - val_accuracy: 0.6989 Epoch 133/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4054 - val_accuracy: 0.6989 Epoch 134/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4054 - val_accuracy: 0.6989 Epoch 00134: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06. Epoch 135/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4054 - val_accuracy: 0.6989 Epoch 136/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4054 - val_accuracy: 0.6989 Epoch 137/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4055 - val_accuracy: 0.6989 Epoch 138/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4055 - val_accuracy: 0.6989 Epoch 139/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4055 - val_accuracy: 0.6989 Epoch 140/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4055 - val_accuracy: 0.6989 Epoch 141/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4056 - val_accuracy: 0.6989 Epoch 142/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4056 - val_accuracy: 0.6989 Epoch 143/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4056 - val_accuracy: 0.6989 Epoch 144/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4056 - val_accuracy: 0.6989 Epoch 00144: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06. Epoch 145/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4056 - val_accuracy: 0.6989 Epoch 146/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 147/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 148/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 149/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 150/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 151/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 152/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 153/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 154/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 00154: ReduceLROnPlateau reducing learning rate to 6.103515488575795e-07. Epoch 155/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 156/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4057 - val_accuracy: 0.6989 Epoch 157/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 158/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 159/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 160/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 161/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 162/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 163/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 164/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00164: ReduceLROnPlateau reducing learning rate to 3.0517577442878974e-07. Epoch 165/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 166/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 167/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 168/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 169/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 170/2000 279/279 [==============================] - ETA: 0s - loss: 0.0231 - accuracy: 1.00 - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 171/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 172/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 173/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 174/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00174: ReduceLROnPlateau reducing learning rate to 1.5258788721439487e-07. Epoch 175/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 176/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 177/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 178/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 179/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 180/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 181/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 182/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 183/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 184/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00184: ReduceLROnPlateau reducing learning rate to 7.629394360719743e-08. Epoch 185/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 186/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 187/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 188/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 189/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 190/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 191/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 192/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 193/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 194/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00194: ReduceLROnPlateau reducing learning rate to 3.814697180359872e-08. Epoch 195/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 196/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 197/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 198/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 199/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 200/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 201/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 202/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 203/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 204/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00204: ReduceLROnPlateau reducing learning rate to 1.907348590179936e-08. Epoch 205/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 206/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 207/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 208/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 209/2000 279/279 [==============================] - ETA: 0s - loss: 0.0149 - accuracy: 1.00 - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 210/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 211/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 212/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 213/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 214/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00214: ReduceLROnPlateau reducing learning rate to 9.53674295089968e-09. Epoch 215/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 216/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 217/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 218/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 219/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 220/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 221/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 222/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 223/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 224/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00224: ReduceLROnPlateau reducing learning rate to 4.76837147544984e-09. Epoch 225/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 226/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 227/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 228/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 229/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 230/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 231/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 232/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 233/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 234/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00234: ReduceLROnPlateau reducing learning rate to 2.38418573772492e-09. Epoch 235/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 236/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 237/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 238/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 239/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 240/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 241/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 242/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 243/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 244/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00244: ReduceLROnPlateau reducing learning rate to 1.19209286886246e-09. Epoch 245/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 246/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 247/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 248/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 249/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 250/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 251/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 252/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 253/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 254/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00254: ReduceLROnPlateau reducing learning rate to 5.9604643443123e-10. Epoch 255/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 256/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 257/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 258/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 259/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 260/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 261/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 262/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 263/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 264/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00264: ReduceLROnPlateau reducing learning rate to 2.98023217215615e-10. Epoch 265/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 266/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 267/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 268/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 269/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 270/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 271/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 272/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 273/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 274/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00274: ReduceLROnPlateau reducing learning rate to 1.490116086078075e-10. Epoch 275/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 276/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 277/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 278/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 279/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 280/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 281/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 282/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 283/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 284/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00284: ReduceLROnPlateau reducing learning rate to 7.450580430390374e-11. Epoch 285/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 286/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 287/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 288/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 289/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 290/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 291/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 292/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 293/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 294/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00294: ReduceLROnPlateau reducing learning rate to 3.725290215195187e-11. Epoch 295/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 296/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 297/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 298/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 299/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 300/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 301/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 302/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 303/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 304/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00304: ReduceLROnPlateau reducing learning rate to 1.8626451075975936e-11. Epoch 305/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 306/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 307/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 308/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 309/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 310/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 311/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 312/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 313/2000 279/279 [==============================] - ETA: 0s - loss: 0.0208 - accuracy: 1.00 - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 314/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00314: ReduceLROnPlateau reducing learning rate to 9.313225537987968e-12. Epoch 315/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 316/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 317/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 318/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 319/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 320/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 321/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 322/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 323/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 324/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00324: ReduceLROnPlateau reducing learning rate to 4.656612768993984e-12. Epoch 325/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 326/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 327/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 328/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 329/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 330/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 331/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 332/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 333/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 334/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00334: ReduceLROnPlateau reducing learning rate to 2.328306384496992e-12. Epoch 335/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 336/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 337/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 338/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 339/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 340/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 341/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 342/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 343/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 344/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00344: ReduceLROnPlateau reducing learning rate to 1.164153192248496e-12. Epoch 345/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 346/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 347/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 348/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 349/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 350/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 351/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 352/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 353/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 354/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00354: ReduceLROnPlateau reducing learning rate to 5.82076596124248e-13. Epoch 355/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 356/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 357/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 358/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 359/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 360/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 361/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 362/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 363/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 364/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00364: ReduceLROnPlateau reducing learning rate to 2.91038298062124e-13. Epoch 365/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 366/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 367/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 368/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 369/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 370/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 371/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 372/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 373/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 374/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00374: ReduceLROnPlateau reducing learning rate to 1.45519149031062e-13. Epoch 375/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 376/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 377/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 378/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 379/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 380/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 381/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 382/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 383/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 384/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00384: ReduceLROnPlateau reducing learning rate to 7.2759574515531e-14. Epoch 385/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 386/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 387/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 388/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 389/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 390/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 391/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 392/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 393/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 394/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00394: ReduceLROnPlateau reducing learning rate to 3.63797872577655e-14. Epoch 395/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 396/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 397/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 398/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 399/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 400/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 401/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 402/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 403/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 404/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00404: ReduceLROnPlateau reducing learning rate to 1.818989362888275e-14. Epoch 405/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 406/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 407/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 408/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 409/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 410/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 411/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 412/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 413/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 414/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00414: ReduceLROnPlateau reducing learning rate to 9.094946814441375e-15. Epoch 415/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 416/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 417/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 418/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 419/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 420/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 421/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 422/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 423/2000 279/279 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 1.00 - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 424/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00424: ReduceLROnPlateau reducing learning rate to 4.5474734072206875e-15. Epoch 425/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 426/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 427/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 428/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 429/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 430/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 431/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 432/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 433/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 434/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00434: ReduceLROnPlateau reducing learning rate to 2.2737367036103438e-15. Epoch 435/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 436/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 437/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 438/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 439/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 440/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 441/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 442/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 443/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 444/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00444: ReduceLROnPlateau reducing learning rate to 1.1368683518051719e-15. Epoch 445/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 446/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 447/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 448/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 449/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 450/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 451/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 452/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 453/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 454/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00454: ReduceLROnPlateau reducing learning rate to 5.684341759025859e-16. Epoch 455/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 456/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 457/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 458/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 459/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 460/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 461/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 462/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 463/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 464/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00464: ReduceLROnPlateau reducing learning rate to 2.8421708795129297e-16. Epoch 465/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 466/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 467/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 468/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 469/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 470/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 471/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 472/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 473/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 474/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00474: ReduceLROnPlateau reducing learning rate to 1.4210854397564648e-16. Epoch 475/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 476/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 477/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 478/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 479/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 480/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 481/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 482/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 483/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 484/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00484: ReduceLROnPlateau reducing learning rate to 7.105427198782324e-17. Epoch 485/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 486/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 487/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 488/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 489/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 490/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 491/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 492/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 493/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 494/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00494: ReduceLROnPlateau reducing learning rate to 3.552713599391162e-17. Epoch 495/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 496/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 497/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 498/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 499/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 500/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 501/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 502/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 503/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 504/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00504: ReduceLROnPlateau reducing learning rate to 1.776356799695581e-17. Epoch 505/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 506/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 507/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 508/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 509/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 510/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 511/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 512/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 513/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 514/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00514: ReduceLROnPlateau reducing learning rate to 8.881783998477905e-18. Epoch 515/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 516/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 517/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 518/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 519/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 520/2000 279/279 [==============================] - 0s 50us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 521/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 522/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 523/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 524/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00524: ReduceLROnPlateau reducing learning rate to 4.440891999238953e-18. Epoch 525/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 526/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 527/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 528/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 529/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 530/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 531/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 532/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 533/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 534/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00534: ReduceLROnPlateau reducing learning rate to 2.2204459996194763e-18. Epoch 535/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 536/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 537/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 538/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 539/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 540/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 541/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 542/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 543/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 544/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00544: ReduceLROnPlateau reducing learning rate to 1.1102229998097382e-18. Epoch 545/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 546/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 547/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 548/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 549/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 550/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 551/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 552/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 553/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 554/2000 279/279 [==============================] - 0s 54us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00554: ReduceLROnPlateau reducing learning rate to 5.551114999048691e-19. Epoch 555/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 556/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 557/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 558/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 559/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 560/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 561/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 562/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 563/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 564/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00564: ReduceLROnPlateau reducing learning rate to 2.7755574995243454e-19. Epoch 565/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 566/2000 279/279 [==============================] - 0s 64us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 567/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 568/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 569/2000 279/279 [==============================] - 0s 57us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 570/2000 279/279 [==============================] - 0s 68us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 571/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 572/2000 279/279 [==============================] - 0s 61us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 573/2000 279/279 [==============================] - 0s 143us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 574/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00574: ReduceLROnPlateau reducing learning rate to 1.3877787497621727e-19. Epoch 575/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 576/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 577/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 578/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 579/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 580/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 581/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 582/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 583/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 584/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00584: ReduceLROnPlateau reducing learning rate to 6.938893748810864e-20. Epoch 585/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 586/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 587/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 588/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 589/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 590/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 591/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 592/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 593/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 594/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00594: ReduceLROnPlateau reducing learning rate to 3.469446874405432e-20. Epoch 595/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 596/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 597/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 598/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 599/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 600/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 601/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 602/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 603/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 604/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00604: ReduceLROnPlateau reducing learning rate to 1.734723437202716e-20. Epoch 605/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 606/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 607/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 608/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 609/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 610/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 611/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 612/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 613/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 614/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00614: ReduceLROnPlateau reducing learning rate to 8.67361718601358e-21. Epoch 615/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 616/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 617/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 618/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 619/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 620/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 621/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 622/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 623/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 624/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00624: ReduceLROnPlateau reducing learning rate to 4.33680859300679e-21. Epoch 625/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 626/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 627/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 628/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 629/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 630/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 631/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 632/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 633/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 634/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00634: ReduceLROnPlateau reducing learning rate to 2.168404296503395e-21. Epoch 635/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 636/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 637/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 638/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 639/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 640/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 641/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 642/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 643/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 644/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00644: ReduceLROnPlateau reducing learning rate to 1.0842021482516974e-21. Epoch 645/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 646/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 647/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 648/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 649/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 650/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 651/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 652/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 653/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 654/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00654: ReduceLROnPlateau reducing learning rate to 5.421010741258487e-22. Epoch 655/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 656/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 657/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 658/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 659/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 660/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 661/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 662/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 663/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 664/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00664: ReduceLROnPlateau reducing learning rate to 2.7105053706292436e-22. Epoch 665/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 666/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 667/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 668/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 669/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 670/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 671/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 672/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 673/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 674/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00674: ReduceLROnPlateau reducing learning rate to 1.3552526853146218e-22. Epoch 675/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 676/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 677/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 678/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 679/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 680/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 681/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 682/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 683/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 684/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00684: ReduceLROnPlateau reducing learning rate to 6.776263426573109e-23. Epoch 685/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 686/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 687/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 688/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 689/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 690/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 691/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 692/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 693/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 694/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00694: ReduceLROnPlateau reducing learning rate to 3.3881317132865545e-23. Epoch 695/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 696/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 697/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 698/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 699/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 700/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 701/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 702/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 703/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 704/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00704: ReduceLROnPlateau reducing learning rate to 1.6940658566432772e-23. Epoch 705/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 706/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 707/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 708/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 709/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 710/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 711/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 712/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 713/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 714/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00714: ReduceLROnPlateau reducing learning rate to 8.470329283216386e-24. Epoch 715/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 716/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 717/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 718/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 719/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 720/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 721/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 722/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 723/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 724/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00724: ReduceLROnPlateau reducing learning rate to 4.235164641608193e-24. Epoch 725/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 726/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 727/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 728/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 729/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 730/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 731/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 732/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 733/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 734/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00734: ReduceLROnPlateau reducing learning rate to 2.1175823208040965e-24. Epoch 735/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 736/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 737/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 738/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 739/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 740/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 741/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 742/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 743/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 744/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00744: ReduceLROnPlateau reducing learning rate to 1.0587911604020483e-24. Epoch 745/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 746/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 747/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 748/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 749/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 750/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 751/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 752/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 753/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 754/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00754: ReduceLROnPlateau reducing learning rate to 5.293955802010241e-25. Epoch 755/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 756/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 757/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 758/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 759/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 760/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 761/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 762/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 763/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 764/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00764: ReduceLROnPlateau reducing learning rate to 2.6469779010051207e-25. Epoch 765/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 766/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 767/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 768/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 769/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 770/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 771/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 772/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 773/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 774/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00774: ReduceLROnPlateau reducing learning rate to 1.3234889505025603e-25. Epoch 775/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 776/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 777/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 778/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 779/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 780/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 781/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 782/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 783/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 784/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00784: ReduceLROnPlateau reducing learning rate to 6.617444752512802e-26. Epoch 785/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 786/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 787/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 788/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 789/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 790/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 791/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 792/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 793/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 794/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00794: ReduceLROnPlateau reducing learning rate to 3.308722376256401e-26. Epoch 795/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 796/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 797/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 798/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 799/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 800/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 801/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 802/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 803/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 804/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00804: ReduceLROnPlateau reducing learning rate to 1.6543611881282004e-26. Epoch 805/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 806/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 807/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 808/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 809/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 810/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 811/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 812/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 813/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 814/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00814: ReduceLROnPlateau reducing learning rate to 8.271805940641002e-27. Epoch 815/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 816/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 817/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 818/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 819/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 820/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 821/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 822/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 823/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 824/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00824: ReduceLROnPlateau reducing learning rate to 4.135902970320501e-27. Epoch 825/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 826/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 827/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 828/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 829/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 830/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 831/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 832/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 833/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 834/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00834: ReduceLROnPlateau reducing learning rate to 2.0679514851602505e-27. Epoch 835/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 836/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 837/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 838/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 839/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 840/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 841/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 842/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 843/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 844/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00844: ReduceLROnPlateau reducing learning rate to 1.0339757425801253e-27. Epoch 845/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 846/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 847/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 848/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 849/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 850/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 851/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 852/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 853/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 854/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00854: ReduceLROnPlateau reducing learning rate to 5.169878712900626e-28. Epoch 855/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 856/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 857/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 858/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 859/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 860/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 861/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 862/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 863/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 864/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00864: ReduceLROnPlateau reducing learning rate to 2.584939356450313e-28. Epoch 865/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 866/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 867/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 868/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 869/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 870/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 871/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 872/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 873/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 874/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00874: ReduceLROnPlateau reducing learning rate to 1.2924696782251566e-28. Epoch 875/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 876/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 877/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 878/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 879/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 880/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 881/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 882/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 883/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 884/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00884: ReduceLROnPlateau reducing learning rate to 6.462348391125783e-29. Epoch 885/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 886/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 887/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 888/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 889/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 890/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 891/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 892/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 893/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 894/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00894: ReduceLROnPlateau reducing learning rate to 3.2311741955628914e-29. Epoch 895/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 896/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 897/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 898/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 899/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 900/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 901/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 902/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 903/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 904/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00904: ReduceLROnPlateau reducing learning rate to 1.6155870977814457e-29. Epoch 905/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 906/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 907/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 908/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 909/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 910/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 911/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 912/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 913/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 914/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00914: ReduceLROnPlateau reducing learning rate to 8.077935488907229e-30. Epoch 915/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 916/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 917/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 918/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 919/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 920/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 921/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 922/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 923/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 924/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00924: ReduceLROnPlateau reducing learning rate to 4.038967744453614e-30. Epoch 925/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 926/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 927/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 928/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 929/2000 279/279 [==============================] - 0s 129us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 930/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 931/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 932/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 933/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 934/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00934: ReduceLROnPlateau reducing learning rate to 2.019483872226807e-30. Epoch 935/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 936/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 937/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 938/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 939/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 940/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 941/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 942/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 943/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 944/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00944: ReduceLROnPlateau reducing learning rate to 1.0097419361134036e-30. Epoch 945/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 946/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 947/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 948/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 949/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 950/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 951/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 952/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 953/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 954/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00954: ReduceLROnPlateau reducing learning rate to 5.048709680567018e-31. Epoch 955/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 956/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 957/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 958/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 959/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 960/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 961/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 962/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 963/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 964/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00964: ReduceLROnPlateau reducing learning rate to 2.524354840283509e-31. Epoch 965/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 966/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 967/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 968/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 969/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 970/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 971/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 972/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 973/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 974/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00974: ReduceLROnPlateau reducing learning rate to 1.2621774201417545e-31. Epoch 975/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 976/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 977/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 978/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 979/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 980/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 981/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 982/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 983/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 984/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00984: ReduceLROnPlateau reducing learning rate to 6.310887100708772e-32. Epoch 985/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 986/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 987/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 988/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 989/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 990/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 991/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 992/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 993/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 994/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 00994: ReduceLROnPlateau reducing learning rate to 3.155443550354386e-32. Epoch 995/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 996/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 997/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 998/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 999/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1000/2000 279/279 [==============================] - 0s 129us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1001/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1002/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1003/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1004/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01004: ReduceLROnPlateau reducing learning rate to 1.577721775177193e-32. Epoch 1005/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1006/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1007/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1008/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1009/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1010/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1011/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1012/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1013/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1014/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01014: ReduceLROnPlateau reducing learning rate to 7.888608875885965e-33. Epoch 1015/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1016/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1017/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1018/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1019/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1020/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1021/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1022/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1023/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1024/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01024: ReduceLROnPlateau reducing learning rate to 3.944304437942983e-33. Epoch 1025/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1026/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1027/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1028/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1029/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1030/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1031/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1032/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1033/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1034/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01034: ReduceLROnPlateau reducing learning rate to 1.9721522189714914e-33. Epoch 1035/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1036/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1037/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1038/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1039/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1040/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1041/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1042/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1043/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1044/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01044: ReduceLROnPlateau reducing learning rate to 9.860761094857457e-34. Epoch 1045/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1046/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1047/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1048/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1049/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1050/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1051/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1052/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1053/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1054/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01054: ReduceLROnPlateau reducing learning rate to 4.930380547428728e-34. Epoch 1055/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1056/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1057/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1058/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1059/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1060/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1061/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1062/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1063/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1064/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01064: ReduceLROnPlateau reducing learning rate to 2.465190273714364e-34. Epoch 1065/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1066/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1067/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1068/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1069/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1070/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1071/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1072/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1073/2000 279/279 [==============================] - ETA: 0s - loss: 0.0214 - accuracy: 1.00 - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1074/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01074: ReduceLROnPlateau reducing learning rate to 1.232595136857182e-34. Epoch 1075/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1076/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1077/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1078/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1079/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1080/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1081/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1082/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1083/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1084/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01084: ReduceLROnPlateau reducing learning rate to 6.16297568428591e-35. Epoch 1085/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1086/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1087/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1088/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1089/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1090/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1091/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1092/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1093/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1094/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01094: ReduceLROnPlateau reducing learning rate to 3.081487842142955e-35. Epoch 1095/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1096/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1097/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1098/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1099/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1100/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1101/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1102/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1103/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1104/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01104: ReduceLROnPlateau reducing learning rate to 1.5407439210714776e-35. Epoch 1105/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1106/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1107/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1108/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1109/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1110/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1111/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1112/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1113/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1114/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01114: ReduceLROnPlateau reducing learning rate to 7.703719605357388e-36. Epoch 1115/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1116/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1117/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1118/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1119/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1120/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1121/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1122/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1123/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1124/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01124: ReduceLROnPlateau reducing learning rate to 3.851859802678694e-36. Epoch 1125/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1126/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1127/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1128/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1129/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1130/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1131/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1132/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1133/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1134/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01134: ReduceLROnPlateau reducing learning rate to 1.925929901339347e-36. Epoch 1135/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1136/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1137/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1138/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1139/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1140/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1141/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1142/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1143/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1144/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01144: ReduceLROnPlateau reducing learning rate to 9.629649506696735e-37. Epoch 1145/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1146/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1147/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1148/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1149/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1150/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1151/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1152/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1153/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1154/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01154: ReduceLROnPlateau reducing learning rate to 4.8148247533483676e-37. Epoch 1155/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1156/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1157/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1158/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1159/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1160/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1161/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1162/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1163/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1164/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01164: ReduceLROnPlateau reducing learning rate to 2.4074123766741838e-37. Epoch 1165/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1166/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1167/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1168/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1169/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1170/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1171/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1172/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1173/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1174/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01174: ReduceLROnPlateau reducing learning rate to 1.2037061883370919e-37. Epoch 1175/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1176/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1177/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1178/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1179/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1180/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1181/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1182/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1183/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1184/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01184: ReduceLROnPlateau reducing learning rate to 6.018530941685459e-38. Epoch 1185/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1186/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1187/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1188/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1189/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1190/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1191/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1192/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1193/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1194/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01194: ReduceLROnPlateau reducing learning rate to 3.0092654708427297e-38. Epoch 1195/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1196/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1197/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1198/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1199/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1200/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1201/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1202/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1203/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1204/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01204: ReduceLROnPlateau reducing learning rate to 1.5046327354213649e-38. Epoch 1205/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1206/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1207/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1208/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1209/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1210/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1211/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1212/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1213/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1214/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01214: ReduceLROnPlateau reducing learning rate to 7.523163677106824e-39. Epoch 1215/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1216/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1217/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1218/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1219/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1220/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1221/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1222/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1223/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1224/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01224: ReduceLROnPlateau reducing learning rate to 3.761581838553412e-39. Epoch 1225/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1226/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1227/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1228/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1229/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1230/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1231/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1232/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1233/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1234/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01234: ReduceLROnPlateau reducing learning rate to 1.88079056895209e-39. Epoch 1235/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1236/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1237/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1238/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1239/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1240/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1241/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1242/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1243/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1244/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01244: ReduceLROnPlateau reducing learning rate to 9.40395284476045e-40. Epoch 1245/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1246/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1247/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1248/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1249/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1250/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1251/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1252/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1253/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1254/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01254: ReduceLROnPlateau reducing learning rate to 4.701972919134064e-40. Epoch 1255/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1256/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1257/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1258/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1259/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1260/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1261/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1262/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1263/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1264/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01264: ReduceLROnPlateau reducing learning rate to 2.350986459567032e-40. Epoch 1265/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1266/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1267/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1268/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1269/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1270/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1271/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1272/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1273/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1274/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01274: ReduceLROnPlateau reducing learning rate to 1.175493229783516e-40. Epoch 1275/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1276/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1277/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1278/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1279/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1280/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1281/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1282/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1283/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1284/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01284: ReduceLROnPlateau reducing learning rate to 5.87746614891758e-41. Epoch 1285/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1286/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1287/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1288/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1289/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1290/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1291/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1292/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1293/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1294/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01294: ReduceLROnPlateau reducing learning rate to 2.93873307445879e-41. Epoch 1295/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1296/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1297/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1298/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1299/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1300/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1301/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1302/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1303/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1304/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01304: ReduceLROnPlateau reducing learning rate to 1.4694015696910032e-41. Epoch 1305/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1306/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1307/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1308/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1309/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1310/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1311/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1312/2000 279/279 [==============================] - 0s 72us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1313/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1314/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01314: ReduceLROnPlateau reducing learning rate to 7.347007848455016e-42. Epoch 1315/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1316/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1317/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1318/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1319/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1320/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1321/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1322/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1323/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1324/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01324: ReduceLROnPlateau reducing learning rate to 3.673503924227508e-42. Epoch 1325/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1326/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1327/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1328/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1329/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1330/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1331/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1332/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1333/2000 279/279 [==============================] - 0s 129us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1334/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01334: ReduceLROnPlateau reducing learning rate to 1.8371022867298352e-42. Epoch 1335/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1336/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1337/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1338/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1339/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1340/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1341/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1342/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1343/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1344/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01344: ReduceLROnPlateau reducing learning rate to 9.185511433649176e-43. Epoch 1345/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1346/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1347/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1348/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1349/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1350/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1351/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1352/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1353/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1354/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01354: ReduceLROnPlateau reducing learning rate to 4.5962589629854e-43. Epoch 1355/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1356/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1357/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1358/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1359/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1360/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1361/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1362/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1363/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1364/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01364: ReduceLROnPlateau reducing learning rate to 2.2981294814927e-43. Epoch 1365/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1366/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1367/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1368/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1369/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1370/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1371/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1372/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1373/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1374/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01374: ReduceLROnPlateau reducing learning rate to 1.14906474074635e-43. Epoch 1375/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1376/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1377/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1378/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1379/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1380/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1381/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1382/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1383/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1384/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01384: ReduceLROnPlateau reducing learning rate to 5.74532370373175e-44. Epoch 1385/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1386/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1387/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1388/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1389/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1390/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1391/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1392/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1393/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1394/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01394: ReduceLROnPlateau reducing learning rate to 2.872661851865875e-44. Epoch 1395/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1396/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1397/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1398/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1399/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1400/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1401/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1402/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1403/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1404/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01404: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-44. Epoch 1405/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1406/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1407/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1408/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1409/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1410/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1411/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1412/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1413/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1414/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01414: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-45. Epoch 1415/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1416/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1417/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1418/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1419/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1420/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1421/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1422/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1423/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1424/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01424: ReduceLROnPlateau reducing learning rate to 3.5032461608120427e-45. Epoch 1425/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1426/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1427/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1428/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1429/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1430/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1431/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1432/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1433/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1434/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01434: ReduceLROnPlateau reducing learning rate to 1.401298464324817e-45. Epoch 1435/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1436/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1437/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1438/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1439/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1440/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1441/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1442/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1443/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1444/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 01444: ReduceLROnPlateau reducing learning rate to 7.006492321624085e-46. Epoch 1445/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1446/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1447/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1448/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1449/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1450/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1451/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1452/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1453/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1454/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1455/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1456/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1457/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1458/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1459/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1460/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1461/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1462/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1463/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1464/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1465/2000 279/279 [==============================] - ETA: 0s - loss: 0.0195 - accuracy: 1.00 - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1466/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1467/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1468/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1469/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1470/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1471/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1472/2000 279/279 [==============================] - 0s 133us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1473/2000 279/279 [==============================] - 0s 136us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1474/2000 279/279 [==============================] - 0s 136us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1475/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1476/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1477/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1478/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1479/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1480/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1481/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1482/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1483/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1484/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1485/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1486/2000 279/279 [==============================] - 0s 129us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1487/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1488/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1489/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1490/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1491/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1492/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1493/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1494/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1495/2000 279/279 [==============================] - 0s 154us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1496/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1497/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1498/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1499/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1500/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1501/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1502/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1503/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1504/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1505/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1506/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1507/2000 279/279 [==============================] - 0s 133us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1508/2000 279/279 [==============================] - 0s 140us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1509/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1510/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1511/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1512/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1513/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1514/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1515/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1516/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1517/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1518/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1519/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1520/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1521/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1522/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1523/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1524/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1525/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1526/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1527/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1528/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1529/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1530/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1531/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1532/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1533/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1534/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1535/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1536/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1537/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1538/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1539/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1540/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1541/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1542/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1543/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1544/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1545/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1546/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1547/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1548/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1549/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1550/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1551/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1552/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1553/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1554/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1555/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1556/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1557/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1558/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1559/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1560/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1561/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1562/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1563/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1564/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1565/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1566/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1567/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1568/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1569/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1570/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1571/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1572/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1573/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1574/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1575/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1576/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1577/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1578/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1579/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1580/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1581/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1582/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1583/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1584/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1585/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1586/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1587/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1588/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1589/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1590/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1591/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1592/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1593/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1594/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1595/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1596/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1597/2000 279/279 [==============================] - 0s 111us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1598/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1599/2000 279/279 [==============================] - 0s 118us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1600/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1601/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1602/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1603/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1604/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1605/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1606/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1607/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1608/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1609/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1610/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1611/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1612/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1613/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1614/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1615/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1616/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1617/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1618/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1619/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1620/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1621/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1622/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1623/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1624/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1625/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1626/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1627/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1628/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1629/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1630/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1631/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1632/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1633/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1634/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1635/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1636/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1637/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1638/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1639/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1640/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1641/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1642/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1643/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1644/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1645/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1646/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1647/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1648/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1649/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1650/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1651/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1652/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1653/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1654/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1655/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1656/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1657/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1658/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1659/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1660/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1661/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1662/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1663/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1664/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1665/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1666/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1667/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1668/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1669/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1670/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1671/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1672/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1673/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1674/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1675/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1676/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1677/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1678/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1679/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1680/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1681/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1682/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1683/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1684/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1685/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1686/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1687/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1688/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1689/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1690/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1691/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1692/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1693/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1694/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1695/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1696/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1697/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1698/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1699/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1700/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1701/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1702/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1703/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1704/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1705/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1706/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1707/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1708/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1709/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1710/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1711/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1712/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1713/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1714/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1715/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1716/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1717/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1718/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1719/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1720/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1721/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1722/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1723/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1724/2000 279/279 [==============================] - 0s 97us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1725/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1726/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1727/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1728/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1729/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1730/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1731/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1732/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1733/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1734/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1735/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1736/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1737/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1738/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1739/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1740/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1741/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1742/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1743/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1744/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1745/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1746/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1747/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1748/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1749/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1750/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1751/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1752/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1753/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1754/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1755/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1756/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1757/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1758/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1759/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1760/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1761/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1762/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1763/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1764/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1765/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1766/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1767/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1768/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1769/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1770/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1771/2000 279/279 [==============================] - 0s 122us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1772/2000 279/279 [==============================] - 0s 143us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1773/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1774/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1775/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1776/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1777/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1778/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1779/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1780/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1781/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1782/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1783/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1784/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1785/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1786/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1787/2000 279/279 [==============================] - 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0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1865/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1866/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1867/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1868/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1869/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1870/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1871/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1872/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1873/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1874/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1875/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1876/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1877/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1878/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1879/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1880/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1881/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1882/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1883/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1884/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1885/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1886/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1887/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1888/2000 279/279 [==============================] - 0s 115us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1889/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1890/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1891/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1892/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1893/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1894/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1895/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1896/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1897/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1898/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1899/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1900/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1901/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1902/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1903/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1904/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1905/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1906/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1907/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1908/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1909/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1910/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1911/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1912/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1913/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1914/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1915/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1916/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1917/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1918/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1919/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1920/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1921/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1922/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1923/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1924/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1925/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1926/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1927/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1928/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1929/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1930/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1931/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1932/2000 279/279 [==============================] - 0s 100us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1933/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1934/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1935/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1936/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1937/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1938/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1939/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1940/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1941/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1942/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1943/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1944/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1945/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1946/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1947/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1948/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1949/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1950/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1951/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1952/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1953/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1954/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1955/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1956/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1957/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1958/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1959/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1960/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1961/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1962/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1963/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1964/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1965/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1966/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1967/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1968/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1969/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1970/2000 279/279 [==============================] - 0s 129us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1971/2000 279/279 [==============================] - 0s 125us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1972/2000 279/279 [==============================] - 0s 107us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1973/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1974/2000 279/279 [==============================] - 0s 104us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1975/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1976/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1977/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1978/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1979/2000 279/279 [==============================] - 0s 75us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1980/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1981/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1982/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1983/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1984/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1985/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1986/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1987/2000 279/279 [==============================] - 0s 90us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1988/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1989/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1990/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1991/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1992/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1993/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1994/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1995/2000 279/279 [==============================] - 0s 82us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1996/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1997/2000 279/279 [==============================] - 0s 79us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1998/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 1999/2000 279/279 [==============================] - 0s 86us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989 Epoch 2000/2000 279/279 [==============================] - 0s 93us/step - loss: 0.0222 - accuracy: 1.0000 - val_loss: 1.4058 - val_accuracy: 0.6989
In [323]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 2000)
In [324]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
93/93 [==============================] - 0s 43us/step test loss: 1.405830939610799, test accuracy: 0.698924720287323
In [325]:
y_pred = model.predict(X_test)
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
Kappa: 0.1712285168682367 AUC ROC: 0.5791176470588235
KMeans¶
In [216]:
X
Out[216]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.574572 | 0.885785 | -0.744040 | 1.222732 | -1.020937 | 0.253925 |
| 1 | 1.110821 | 0.925768 | 0.226451 | -0.119687 | 0.143401 | -0.009902 |
| 2 | -0.106107 | 1.808295 | 1.961049 | 1.107464 | 2.076449 | 1.966210 |
| 3 | 0.083078 | 1.385239 | 1.506771 | 1.294360 | 1.104665 | 2.105667 |
| 4 | -0.164331 | 0.169248 | 0.525026 | 1.442347 | 1.710639 | 0.657093 |
| 5 | -0.601767 | 0.100025 | 2.894764 | -1.234721 | 1.277722 | 2.254758 |
| 6 | -0.436923 | -0.300019 | 1.146480 | 0.730610 | -1.634206 | -0.621955 |
| 7 | -0.587280 | 1.210009 | 0.829948 | 0.235398 | -0.830262 | 0.277419 |
| 8 | -0.072320 | 0.439239 | -0.084262 | 0.666161 | -0.979709 | 0.134482 |
| 9 | 0.177494 | 0.386052 | 0.266785 | -1.461050 | 1.702079 | -0.627335 |
| 10 | 0.473878 | 0.893926 | -0.138418 | -0.267275 | -0.407548 | -0.229186 |
| 11 | 0.245249 | 0.482974 | 0.995106 | 0.679754 | 0.235560 | 0.480101 |
| 12 | 0.011981 | -0.373717 | -0.589054 | 0.487517 | -1.428960 | 0.073724 |
| 13 | 0.190041 | -0.273603 | 0.483229 | 0.925167 | -1.268062 | -0.057357 |
| 14 | -1.064192 | -0.043564 | -1.313412 | -1.204309 | 1.571772 | -1.751836 |
| 15 | -0.197842 | 0.773898 | 0.917595 | -0.533388 | 1.883323 | -0.803595 |
| 16 | -0.610344 | 0.615674 | 0.638901 | -1.525221 | 1.272377 | -1.132221 |
| 17 | 0.804488 | -1.479012 | 0.374228 | 0.166272 | -1.743433 | -1.002346 |
| 18 | 0.490692 | -1.982800 | -0.516405 | -0.202546 | -1.666137 | -1.170162 |
| 19 | 0.592053 | -1.492906 | 0.420008 | -0.901877 | -2.327543 | -0.070308 |
| 20 | -1.787738 | 1.285484 | -0.785859 | -2.380832 | -1.022434 | -0.395605 |
| 21 | -1.742165 | -0.033766 | -0.693835 | -0.641834 | 0.381590 | -1.954523 |
| 22 | -1.729041 | 0.173705 | -1.150118 | -0.130491 | -1.173120 | -1.443805 |
| 23 | 0.437142 | 1.722799 | -2.129021 | -2.481456 | -0.156650 | 0.254809 |
| 24 | 1.350380 | 0.970678 | 0.076009 | -0.404025 | -1.384857 | 0.117089 |
| 25 | 0.496482 | -0.133100 | -0.887460 | 0.472889 | -1.490365 | 1.615562 |
| 26 | 0.364827 | 1.228853 | -0.931602 | -0.240277 | -0.555015 | 1.259771 |
| 27 | 1.022426 | 1.569202 | -1.345165 | -1.077121 | -0.192695 | 0.678057 |
| 28 | 0.458228 | 1.620487 | -0.211045 | -1.256812 | 0.846741 | -0.038512 |
| 29 | 0.607951 | 1.683390 | -0.591685 | -0.243413 | -0.937265 | 2.063508 |
| ... | ... | ... | ... | ... | ... | ... |
| 342 | 0.736233 | -1.887137 | 0.319724 | -0.489954 | -0.346230 | -0.577137 |
| 343 | 2.234360 | -0.046651 | 1.657368 | -1.085388 | 0.721768 | -1.665035 |
| 344 | 0.911113 | 0.039319 | -1.297534 | 0.592375 | -0.241987 | 2.261413 |
| 345 | 1.384636 | -0.476054 | -1.183101 | 0.367974 | 0.257066 | 1.378080 |
| 346 | 0.961181 | 0.071015 | -0.810140 | 0.868325 | 0.332780 | 1.588246 |
| 347 | 0.937051 | -1.450311 | -0.319702 | -0.228450 | 0.739750 | -0.449282 |
| 348 | -3.281761 | 0.179415 | -1.446642 | -0.685115 | -0.600794 | 0.658153 |
| 349 | -2.344820 | 0.180118 | 0.106773 | 0.037180 | -0.877178 | 1.123398 |
| 350 | 1.460135 | 0.906456 | -0.147713 | 0.343900 | -0.620485 | 0.700956 |
| 351 | 2.114961 | 0.965145 | -1.378351 | -0.574489 | -1.693320 | -0.016307 |
| 352 | 1.504768 | 0.846661 | -1.860231 | 0.705179 | -1.292241 | 0.593433 |
| 353 | -0.213580 | 0.437840 | 0.427356 | 1.095762 | -1.001309 | -0.054969 |
| 354 | -0.063245 | 0.024794 | 0.060996 | 0.129995 | -0.599389 | 0.500376 |
| 355 | 0.116032 | 0.004823 | 0.235041 | 0.085659 | -0.431386 | 0.814703 |
| 356 | -0.330558 | 0.187426 | -0.234587 | 0.942953 | -0.434096 | 0.203910 |
| 357 | 0.380368 | 0.608654 | -0.308912 | 1.154966 | -0.255135 | 0.167498 |
| 358 | 0.893056 | 0.389669 | -0.426997 | 0.638788 | -0.700280 | 0.368667 |
| 359 | -1.019517 | -2.497618 | 0.166376 | 1.273368 | 0.313702 | -0.420230 |
| 360 | -0.784247 | -1.148191 | -0.802374 | 1.038236 | -0.602589 | -0.913446 |
| 361 | 0.139494 | -2.036594 | -1.137199 | -0.379348 | -0.257913 | 0.506162 |
| 362 | 1.494839 | -2.022204 | -0.164524 | -2.180060 | 0.002713 | 1.550614 |
| 363 | 3.128156 | -1.231830 | 0.035160 | -2.785380 | -0.610055 | 0.724620 |
| 364 | 2.010326 | -1.482568 | -1.697983 | -2.728569 | -0.765820 | 2.873139 |
| 365 | 1.499390 | 0.641291 | -0.739018 | -1.456660 | -0.760400 | -0.452027 |
| 366 | 1.398136 | 1.715250 | -0.369182 | -1.280480 | -0.150680 | -0.884280 |
| 367 | 1.103318 | 0.778728 | -0.851121 | -1.368219 | 0.142626 | -0.918794 |
| 368 | 0.303168 | 0.188358 | 0.095953 | -0.024506 | -0.709672 | -1.109607 |
| 369 | 1.183673 | 0.747660 | -0.209307 | 0.329011 | -1.151082 | -0.726250 |
| 370 | -0.723654 | -0.290377 | 1.173636 | -0.123624 | 1.997744 | -0.687810 |
| 371 | -0.073840 | 1.011128 | 0.445136 | 0.821330 | -0.338478 | -0.694080 |
372 rows × 6 columns
In [217]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[217]:
[2232.0, 1860.320016741722, 1609.923817414493, 1413.3293251834796, 1299.2594621943642, 1187.9595776711437, 1107.911820622218, 1042.5800837127117, 995.2457858732439, 962.2198115266042, 937.080734625301, 898.72618782214, 866.9993526105154, 833.7799456003584]
In [218]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[218]:
[<matplotlib.lines.Line2D at 0x1e82f9e3828>]
K=4
In [219]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[219]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [220]:
kmeans_tc.labels_
Out[220]:
array([2, 2, 0, 0, 0, 0, 1, 0, 0, 3, 2, 0, 1, 1, 3, 3, 3, 1, 1, 1, 3, 3,
1, 3, 2, 2, 2, 2, 3, 2, 0, 0, 3, 3, 1, 1, 1, 1, 1, 1, 0, 0, 0, 3,
3, 3, 2, 2, 2, 2, 2, 0, 1, 1, 0, 0, 0, 2, 2, 2, 3, 1, 3, 1, 3, 3,
0, 0, 0, 3, 3, 3, 3, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 3, 3, 0, 0,
2, 1, 0, 3, 2, 1, 1, 2, 0, 3, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 3, 0, 2, 2, 2, 0, 3, 2, 0, 3, 2, 3, 1, 1, 1, 0, 2, 0,
3, 3, 3, 3, 1, 2, 0, 1, 0, 1, 1, 2, 1, 0, 1, 1, 0, 0, 0, 3, 0, 2,
0, 0, 0, 1, 1, 2, 0, 0, 0, 1, 1, 1, 2, 2, 1, 3, 2, 3, 0, 1, 1, 2,
0, 1, 1, 1, 1, 2, 1, 2, 2, 3, 3, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 1,
2, 1, 1, 3, 0, 1, 2, 3, 2, 2, 3, 2, 0, 2, 2, 1, 0, 1, 0, 1, 2, 1,
2, 3, 3, 3, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 3, 2, 3, 3, 3, 3, 3,
3, 3, 3, 1, 0, 0, 2, 2, 2, 1, 2, 2, 3, 3, 3, 1, 2, 2, 3, 3, 3, 3,
2, 1, 1, 1, 3, 3, 1, 1, 1, 2, 2, 1, 3, 2, 0, 2, 2, 3, 3, 3, 3, 1,
0, 2, 2, 3, 2, 0, 3, 1, 3, 2, 1, 0, 0, 3, 0, 0, 0, 2, 0, 1, 1, 0,
0, 0, 3, 0, 0, 1, 1, 3, 1, 1, 3, 0, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0,
0, 3, 0, 0, 0, 2, 2, 3, 2, 1, 1, 1, 1, 3, 2, 2, 2, 1, 0, 0, 2, 2,
2, 0, 0, 0, 0, 2, 2, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 2, 3, 0])
In [221]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[221]:
array([2, 2, 0, 0, 0, 0, 1, 0, 0, 3, 2, 0, 1, 1, 3, 3, 3, 1, 1, 1, 3, 3,
1, 3, 2, 2, 2, 2, 3, 2, 0, 0, 3, 3, 1, 1, 1, 1, 1, 1, 0, 0, 0, 3,
3, 3, 2, 2, 2, 2, 2, 0, 1, 1, 0, 0, 0, 2, 2, 2, 3, 1, 3, 1, 3, 3,
0, 0, 0, 3, 3, 3, 3, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 3, 3, 0, 0,
2, 1, 0, 3, 2, 1, 1, 2, 0, 3, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0,
3, 0, 0, 0, 3, 0, 2, 2, 2, 0, 3, 2, 0, 3, 2, 3, 1, 1, 1, 0, 2, 0,
3, 3, 3, 3, 1, 2, 0, 1, 0, 1, 1, 2, 1, 0, 1, 1, 0, 0, 0, 3, 0, 2,
0, 0, 0, 1, 1, 2, 0, 0, 0, 1, 1, 1, 2, 2, 1, 3, 2, 3, 0, 1, 1, 2,
0, 1, 1, 1, 1, 2, 1, 2, 2, 3, 3, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 1,
2, 1, 1, 3, 0, 1, 2, 3, 2, 2, 3, 2, 0, 2, 2, 1, 0, 1, 0, 1, 2, 1,
2, 3, 3, 3, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 3, 2, 3, 3, 3, 3, 3,
3, 3, 3, 1, 0, 0, 2, 2, 2, 1, 2, 2, 3, 3, 3, 1, 2, 2, 3, 3, 3, 3,
2, 1, 1, 1, 3, 3, 1, 1, 1, 2, 2, 1, 3, 2, 0, 2, 2, 3, 3, 3, 3, 1,
0, 2, 2, 3, 2, 0, 3, 1, 3, 2, 1, 0, 0, 3, 0, 0, 0, 2, 0, 1, 1, 0,
0, 0, 3, 0, 0, 1, 1, 3, 1, 1, 3, 0, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0,
0, 3, 0, 0, 0, 2, 2, 3, 2, 1, 1, 1, 1, 3, 2, 2, 2, 1, 0, 0, 2, 2,
2, 0, 0, 0, 0, 2, 2, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 2, 3, 0])
In [222]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [223]:
X
Out[223]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.574572 | 0.885785 | -0.744040 | 1.222732 | -1.020937 | 0.253925 | 2 | 0 |
| 1 | 1.110821 | 0.925768 | 0.226451 | -0.119687 | 0.143401 | -0.009902 | 2 | 0 |
| 2 | -0.106107 | 1.808295 | 1.961049 | 1.107464 | 2.076449 | 1.966210 | 0 | 0 |
| 3 | 0.083078 | 1.385239 | 1.506771 | 1.294360 | 1.104665 | 2.105667 | 0 | 0 |
| 4 | -0.164331 | 0.169248 | 0.525026 | 1.442347 | 1.710639 | 0.657093 | 0 | 0 |
| 5 | -0.601767 | 0.100025 | 2.894764 | -1.234721 | 1.277722 | 2.254758 | 0 | 0 |
| 6 | -0.436923 | -0.300019 | 1.146480 | 0.730610 | -1.634206 | -0.621955 | 1 | 0 |
| 7 | -0.587280 | 1.210009 | 0.829948 | 0.235398 | -0.830262 | 0.277419 | 0 | 0 |
| 8 | -0.072320 | 0.439239 | -0.084262 | 0.666161 | -0.979709 | 0.134482 | 0 | 0 |
| 9 | 0.177494 | 0.386052 | 0.266785 | -1.461050 | 1.702079 | -0.627335 | 3 | 0 |
| 10 | 0.473878 | 0.893926 | -0.138418 | -0.267275 | -0.407548 | -0.229186 | 2 | 0 |
| 11 | 0.245249 | 0.482974 | 0.995106 | 0.679754 | 0.235560 | 0.480101 | 0 | 0 |
| 12 | 0.011981 | -0.373717 | -0.589054 | 0.487517 | -1.428960 | 0.073724 | 1 | 0 |
| 13 | 0.190041 | -0.273603 | 0.483229 | 0.925167 | -1.268062 | -0.057357 | 1 | 0 |
| 14 | -1.064192 | -0.043564 | -1.313412 | -1.204309 | 1.571772 | -1.751836 | 3 | 0 |
| 15 | -0.197842 | 0.773898 | 0.917595 | -0.533388 | 1.883323 | -0.803595 | 3 | 0 |
| 16 | -0.610344 | 0.615674 | 0.638901 | -1.525221 | 1.272377 | -1.132221 | 3 | 0 |
| 17 | 0.804488 | -1.479012 | 0.374228 | 0.166272 | -1.743433 | -1.002346 | 1 | 0 |
| 18 | 0.490692 | -1.982800 | -0.516405 | -0.202546 | -1.666137 | -1.170162 | 1 | 0 |
| 19 | 0.592053 | -1.492906 | 0.420008 | -0.901877 | -2.327543 | -0.070308 | 1 | 0 |
| 20 | -1.787738 | 1.285484 | -0.785859 | -2.380832 | -1.022434 | -0.395605 | 3 | 0 |
| 21 | -1.742165 | -0.033766 | -0.693835 | -0.641834 | 0.381590 | -1.954523 | 3 | 0 |
| 22 | -1.729041 | 0.173705 | -1.150118 | -0.130491 | -1.173120 | -1.443805 | 1 | 0 |
| 23 | 0.437142 | 1.722799 | -2.129021 | -2.481456 | -0.156650 | 0.254809 | 3 | 0 |
| 24 | 1.350380 | 0.970678 | 0.076009 | -0.404025 | -1.384857 | 0.117089 | 2 | 0 |
| 25 | 0.496482 | -0.133100 | -0.887460 | 0.472889 | -1.490365 | 1.615562 | 2 | 0 |
| 26 | 0.364827 | 1.228853 | -0.931602 | -0.240277 | -0.555015 | 1.259771 | 2 | 0 |
| 27 | 1.022426 | 1.569202 | -1.345165 | -1.077121 | -0.192695 | 0.678057 | 2 | 0 |
| 28 | 0.458228 | 1.620487 | -0.211045 | -1.256812 | 0.846741 | -0.038512 | 3 | 0 |
| 29 | 0.607951 | 1.683390 | -0.591685 | -0.243413 | -0.937265 | 2.063508 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 342 | 0.736233 | -1.887137 | 0.319724 | -0.489954 | -0.346230 | -0.577137 | 1 | 1 |
| 343 | 2.234360 | -0.046651 | 1.657368 | -1.085388 | 0.721768 | -1.665035 | 3 | 1 |
| 344 | 0.911113 | 0.039319 | -1.297534 | 0.592375 | -0.241987 | 2.261413 | 2 | 1 |
| 345 | 1.384636 | -0.476054 | -1.183101 | 0.367974 | 0.257066 | 1.378080 | 2 | 1 |
| 346 | 0.961181 | 0.071015 | -0.810140 | 0.868325 | 0.332780 | 1.588246 | 2 | 1 |
| 347 | 0.937051 | -1.450311 | -0.319702 | -0.228450 | 0.739750 | -0.449282 | 1 | 1 |
| 348 | -3.281761 | 0.179415 | -1.446642 | -0.685115 | -0.600794 | 0.658153 | 0 | 1 |
| 349 | -2.344820 | 0.180118 | 0.106773 | 0.037180 | -0.877178 | 1.123398 | 0 | 1 |
| 350 | 1.460135 | 0.906456 | -0.147713 | 0.343900 | -0.620485 | 0.700956 | 2 | 1 |
| 351 | 2.114961 | 0.965145 | -1.378351 | -0.574489 | -1.693320 | -0.016307 | 2 | 1 |
| 352 | 1.504768 | 0.846661 | -1.860231 | 0.705179 | -1.292241 | 0.593433 | 2 | 1 |
| 353 | -0.213580 | 0.437840 | 0.427356 | 1.095762 | -1.001309 | -0.054969 | 0 | 1 |
| 354 | -0.063245 | 0.024794 | 0.060996 | 0.129995 | -0.599389 | 0.500376 | 0 | 1 |
| 355 | 0.116032 | 0.004823 | 0.235041 | 0.085659 | -0.431386 | 0.814703 | 0 | 1 |
| 356 | -0.330558 | 0.187426 | -0.234587 | 0.942953 | -0.434096 | 0.203910 | 0 | 1 |
| 357 | 0.380368 | 0.608654 | -0.308912 | 1.154966 | -0.255135 | 0.167498 | 2 | 1 |
| 358 | 0.893056 | 0.389669 | -0.426997 | 0.638788 | -0.700280 | 0.368667 | 2 | 1 |
| 359 | -1.019517 | -2.497618 | 0.166376 | 1.273368 | 0.313702 | -0.420230 | 1 | 1 |
| 360 | -0.784247 | -1.148191 | -0.802374 | 1.038236 | -0.602589 | -0.913446 | 1 | 1 |
| 361 | 0.139494 | -2.036594 | -1.137199 | -0.379348 | -0.257913 | 0.506162 | 1 | 1 |
| 362 | 1.494839 | -2.022204 | -0.164524 | -2.180060 | 0.002713 | 1.550614 | 2 | 1 |
| 363 | 3.128156 | -1.231830 | 0.035160 | -2.785380 | -0.610055 | 0.724620 | 2 | 1 |
| 364 | 2.010326 | -1.482568 | -1.697983 | -2.728569 | -0.765820 | 2.873139 | 2 | 1 |
| 365 | 1.499390 | 0.641291 | -0.739018 | -1.456660 | -0.760400 | -0.452027 | 2 | 1 |
| 366 | 1.398136 | 1.715250 | -0.369182 | -1.280480 | -0.150680 | -0.884280 | 3 | 1 |
| 367 | 1.103318 | 0.778728 | -0.851121 | -1.368219 | 0.142626 | -0.918794 | 3 | 1 |
| 368 | 0.303168 | 0.188358 | 0.095953 | -0.024506 | -0.709672 | -1.109607 | 1 | 1 |
| 369 | 1.183673 | 0.747660 | -0.209307 | 0.329011 | -1.151082 | -0.726250 | 2 | 1 |
| 370 | -0.723654 | -0.290377 | 1.173636 | -0.123624 | 1.997744 | -0.687810 | 3 | 1 |
| 371 | -0.073840 | 1.011128 | 0.445136 | 0.821330 | -0.338478 | -0.694080 | 0 | 1 |
372 rows × 8 columns
In [224]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[224]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82fa1f9e8>
In [326]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [327]:
X = df_n_ps_std_tc[1]
In [328]:
y = df_n_ps[1]['chosen']
In [329]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [330]:
X_train.shape
Out[330]:
(191, 6)
In [229]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [230]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [231]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [232]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.004, 'max_iter': 400}, que permiten obtener un Accuracy de 77.49% y un Kappa del 29.13
Tiempo total: 27.26 minutos
In [331]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.004
epochs = 400
In [332]:
input_tensor = Input(shape = (n0,))
In [333]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [334]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [335]:
model.summary()
Model: "model_16" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_16 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_53 (Dense) (None, 10) 70 _________________________________________________________________ dense_54 (Dense) (None, 1) 11 ================================================================= Total params: 81 Trainable params: 81 Non-trainable params: 0 _________________________________________________________________
In [336]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/400 191/191 [==============================] - 0s 691us/step - loss: 0.7329 - accuracy: 0.4346 - val_loss: 0.6526 - val_accuracy: 0.6250 Epoch 2/400 191/191 [==============================] - 0s 63us/step - loss: 0.6918 - accuracy: 0.5340 - val_loss: 0.6515 - val_accuracy: 0.6250 Epoch 3/400 191/191 [==============================] - 0s 52us/step - loss: 0.6588 - accuracy: 0.6754 - val_loss: 0.6536 - val_accuracy: 0.6406 Epoch 4/400 191/191 [==============================] - 0s 58us/step - loss: 0.6310 - accuracy: 0.7225 - val_loss: 0.6568 - val_accuracy: 0.6406 Epoch 5/400 191/191 [==============================] - 0s 52us/step - loss: 0.6102 - accuracy: 0.7277 - val_loss: 0.6612 - val_accuracy: 0.6094 Epoch 6/400 191/191 [==============================] - 0s 58us/step - loss: 0.5927 - accuracy: 0.7435 - val_loss: 0.6655 - val_accuracy: 0.6094 Epoch 7/400 191/191 [==============================] - 0s 52us/step - loss: 0.5802 - accuracy: 0.7330 - val_loss: 0.6705 - val_accuracy: 0.6250 Epoch 8/400 191/191 [==============================] - 0s 63us/step - loss: 0.5688 - accuracy: 0.7225 - val_loss: 0.6735 - val_accuracy: 0.6406 Epoch 9/400 191/191 [==============================] - 0s 58us/step - loss: 0.5619 - accuracy: 0.7277 - val_loss: 0.6773 - val_accuracy: 0.6406 Epoch 10/400 191/191 [==============================] - 0s 52us/step - loss: 0.5544 - accuracy: 0.7225 - val_loss: 0.6793 - val_accuracy: 0.6406 Epoch 11/400 191/191 [==============================] - 0s 52us/step - loss: 0.5482 - accuracy: 0.7330 - val_loss: 0.6820 - val_accuracy: 0.6406 Epoch 12/400 191/191 [==============================] - 0s 52us/step - loss: 0.5439 - accuracy: 0.7382 - val_loss: 0.6808 - val_accuracy: 0.6406 Epoch 13/400 191/191 [==============================] - 0s 47us/step - loss: 0.5396 - accuracy: 0.7487 - val_loss: 0.6820 - val_accuracy: 0.6719 Epoch 14/400 191/191 [==============================] - 0s 52us/step - loss: 0.5368 - accuracy: 0.7487 - val_loss: 0.6822 - val_accuracy: 0.6875 Epoch 15/400 191/191 [==============================] - 0s 52us/step - loss: 0.5339 - accuracy: 0.7487 - val_loss: 0.6824 - val_accuracy: 0.6875 Epoch 16/400 191/191 [==============================] - 0s 52us/step - loss: 0.5321 - accuracy: 0.7487 - val_loss: 0.6854 - val_accuracy: 0.6875 Epoch 17/400 191/191 [==============================] - 0s 58us/step - loss: 0.5302 - accuracy: 0.7487 - val_loss: 0.6852 - val_accuracy: 0.7031 Epoch 18/400 191/191 [==============================] - 0s 63us/step - loss: 0.5283 - accuracy: 0.7592 - val_loss: 0.6866 - val_accuracy: 0.6875 Epoch 19/400 191/191 [==============================] - 0s 52us/step - loss: 0.5269 - accuracy: 0.7644 - val_loss: 0.6868 - val_accuracy: 0.6875 Epoch 20/400 191/191 [==============================] - 0s 58us/step - loss: 0.5255 - accuracy: 0.7696 - val_loss: 0.6862 - val_accuracy: 0.6875 Epoch 21/400 191/191 [==============================] - 0s 52us/step - loss: 0.5243 - accuracy: 0.7696 - val_loss: 0.6876 - val_accuracy: 0.7031 Epoch 22/400 191/191 [==============================] - 0s 52us/step - loss: 0.5230 - accuracy: 0.7592 - val_loss: 0.6868 - val_accuracy: 0.7031 Epoch 23/400 191/191 [==============================] - 0s 58us/step - loss: 0.5218 - accuracy: 0.7592 - val_loss: 0.6851 - val_accuracy: 0.6875 Epoch 24/400 191/191 [==============================] - 0s 52us/step - loss: 0.5210 - accuracy: 0.7592 - val_loss: 0.6850 - val_accuracy: 0.6875 Epoch 25/400 191/191 [==============================] - 0s 58us/step - loss: 0.5197 - accuracy: 0.7592 - val_loss: 0.6881 - val_accuracy: 0.6875 Epoch 26/400 191/191 [==============================] - 0s 58us/step - loss: 0.5187 - accuracy: 0.7592 - val_loss: 0.6851 - val_accuracy: 0.6719 Epoch 27/400 191/191 [==============================] - 0s 52us/step - loss: 0.5172 - accuracy: 0.7539 - val_loss: 0.6843 - val_accuracy: 0.6875 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 28/400 191/191 [==============================] - 0s 52us/step - loss: 0.5162 - accuracy: 0.7644 - val_loss: 0.6851 - val_accuracy: 0.6875 Epoch 29/400 191/191 [==============================] - 0s 68us/step - loss: 0.5159 - accuracy: 0.7644 - val_loss: 0.6847 - val_accuracy: 0.6875 Epoch 30/400 191/191 [==============================] - 0s 78us/step - loss: 0.5152 - accuracy: 0.7539 - val_loss: 0.6848 - val_accuracy: 0.6875 Epoch 31/400 191/191 [==============================] - 0s 63us/step - loss: 0.5147 - accuracy: 0.7539 - val_loss: 0.6859 - val_accuracy: 0.6875 Epoch 32/400 191/191 [==============================] - 0s 84us/step - loss: 0.5142 - accuracy: 0.7487 - val_loss: 0.6866 - val_accuracy: 0.6875 Epoch 33/400 191/191 [==============================] - 0s 63us/step - loss: 0.5136 - accuracy: 0.7487 - val_loss: 0.6859 - val_accuracy: 0.6875 Epoch 34/400 191/191 [==============================] - 0s 89us/step - loss: 0.5130 - accuracy: 0.7487 - val_loss: 0.6860 - val_accuracy: 0.6875 Epoch 35/400 191/191 [==============================] - 0s 89us/step - loss: 0.5125 - accuracy: 0.7487 - val_loss: 0.6856 - val_accuracy: 0.6875 Epoch 36/400 191/191 [==============================] - 0s 78us/step - loss: 0.5121 - accuracy: 0.7539 - val_loss: 0.6865 - val_accuracy: 0.6875 Epoch 37/400 191/191 [==============================] - 0s 68us/step - loss: 0.5115 - accuracy: 0.7539 - val_loss: 0.6854 - val_accuracy: 0.6719 Epoch 00037: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 38/400 191/191 [==============================] - 0s 58us/step - loss: 0.5108 - accuracy: 0.7539 - val_loss: 0.6854 - val_accuracy: 0.6719 Epoch 39/400 191/191 [==============================] - 0s 58us/step - loss: 0.5105 - accuracy: 0.7487 - val_loss: 0.6852 - val_accuracy: 0.6719 Epoch 40/400 191/191 [==============================] - 0s 58us/step - loss: 0.5102 - accuracy: 0.7539 - val_loss: 0.6857 - val_accuracy: 0.6719 Epoch 41/400 191/191 [==============================] - 0s 58us/step - loss: 0.5099 - accuracy: 0.7539 - val_loss: 0.6859 - val_accuracy: 0.6719 Epoch 42/400 191/191 [==============================] - 0s 58us/step - loss: 0.5096 - accuracy: 0.7539 - val_loss: 0.6858 - val_accuracy: 0.6719 Epoch 43/400 191/191 [==============================] - 0s 58us/step - loss: 0.5095 - accuracy: 0.7487 - val_loss: 0.6846 - val_accuracy: 0.6719 Epoch 44/400 191/191 [==============================] - 0s 58us/step - loss: 0.5091 - accuracy: 0.7539 - val_loss: 0.6850 - val_accuracy: 0.6719 Epoch 45/400 191/191 [==============================] - 0s 63us/step - loss: 0.5089 - accuracy: 0.7539 - val_loss: 0.6851 - val_accuracy: 0.6719 Epoch 46/400 191/191 [==============================] - 0s 58us/step - loss: 0.5085 - accuracy: 0.7539 - val_loss: 0.6851 - val_accuracy: 0.6719 Epoch 47/400 191/191 [==============================] - 0s 63us/step - loss: 0.5083 - accuracy: 0.7539 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 48/400 191/191 [==============================] - 0s 58us/step - loss: 0.5079 - accuracy: 0.7487 - val_loss: 0.6850 - val_accuracy: 0.6719 Epoch 49/400 191/191 [==============================] - 0s 52us/step - loss: 0.5077 - accuracy: 0.7539 - val_loss: 0.6850 - val_accuracy: 0.6719 Epoch 50/400 191/191 [==============================] - 0s 58us/step - loss: 0.5076 - accuracy: 0.7539 - val_loss: 0.6852 - val_accuracy: 0.6719 Epoch 51/400 191/191 [==============================] - 0s 63us/step - loss: 0.5075 - accuracy: 0.7487 - val_loss: 0.6853 - val_accuracy: 0.6719 Epoch 52/400 191/191 [==============================] - 0s 58us/step - loss: 0.5073 - accuracy: 0.7487 - val_loss: 0.6853 - val_accuracy: 0.6719 Epoch 53/400 191/191 [==============================] - 0s 58us/step - loss: 0.5072 - accuracy: 0.7487 - val_loss: 0.6851 - val_accuracy: 0.6719 Epoch 54/400 191/191 [==============================] - 0s 52us/step - loss: 0.5070 - accuracy: 0.7487 - val_loss: 0.6850 - val_accuracy: 0.6719 Epoch 55/400 191/191 [==============================] - 0s 58us/step - loss: 0.5069 - accuracy: 0.7487 - val_loss: 0.6851 - val_accuracy: 0.6719 Epoch 56/400 191/191 [==============================] - 0s 58us/step - loss: 0.5067 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 57/400 191/191 [==============================] - 0s 52us/step - loss: 0.5066 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 58/400 191/191 [==============================] - 0s 73us/step - loss: 0.5064 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 59/400 191/191 [==============================] - 0s 73us/step - loss: 0.5063 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 60/400 191/191 [==============================] - 0s 68us/step - loss: 0.5063 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 61/400 191/191 [==============================] - 0s 58us/step - loss: 0.5062 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 62/400 191/191 [==============================] - 0s 89us/step - loss: 0.5061 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 63/400 191/191 [==============================] - 0s 110us/step - loss: 0.5060 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 64/400 191/191 [==============================] - 0s 84us/step - loss: 0.5060 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 65/400 191/191 [==============================] - 0s 78us/step - loss: 0.5059 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 66/400 191/191 [==============================] - 0s 73us/step - loss: 0.5058 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 67/400 191/191 [==============================] - 0s 73us/step - loss: 0.5058 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 68/400 191/191 [==============================] - 0s 68us/step - loss: 0.5057 - accuracy: 0.7487 - val_loss: 0.6846 - val_accuracy: 0.6719 Epoch 69/400 191/191 [==============================] - 0s 63us/step - loss: 0.5056 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 70/400 191/191 [==============================] - 0s 68us/step - loss: 0.5056 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 71/400 191/191 [==============================] - 0s 79us/step - loss: 0.5056 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 72/400 191/191 [==============================] - 0s 68us/step - loss: 0.5055 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 73/400 191/191 [==============================] - 0s 78us/step - loss: 0.5055 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 74/400 191/191 [==============================] - 0s 89us/step - loss: 0.5055 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 75/400 191/191 [==============================] - 0s 68us/step - loss: 0.5054 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 76/400 191/191 [==============================] - 0s 63us/step - loss: 0.5054 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 77/400 191/191 [==============================] - 0s 63us/step - loss: 0.5053 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 00077: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 78/400 191/191 [==============================] - 0s 58us/step - loss: 0.5053 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 79/400 191/191 [==============================] - 0s 52us/step - loss: 0.5053 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 80/400 191/191 [==============================] - 0s 68us/step - loss: 0.5053 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 81/400 191/191 [==============================] - 0s 68us/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 82/400 191/191 [==============================] - 0s 58us/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.6848 - val_accuracy: 0.6719 Epoch 83/400 191/191 [==============================] - 0s 63us/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 84/400 191/191 [==============================] - 0s 58us/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 85/400 191/191 [==============================] - 0s 63us/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 86/400 191/191 [==============================] - 0s 58us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 87/400 191/191 [==============================] - 0s 52us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00087: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 88/400 191/191 [==============================] - 0s 52us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 89/400 191/191 [==============================] - 0s 58us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 90/400 191/191 [==============================] - 0s 58us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 91/400 191/191 [==============================] - 0s 52us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 92/400 191/191 [==============================] - 0s 58us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 93/400 191/191 [==============================] - 0s 63us/step - loss: 0.5051 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 94/400 191/191 [==============================] - 0s 58us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 95/400 191/191 [==============================] - 0s 52us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 96/400 191/191 [==============================] - 0s 58us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 97/400 191/191 [==============================] - 0s 110us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00097: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 98/400 191/191 [==============================] - 0s 58us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 99/400 191/191 [==============================] - 0s 58us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 100/400 191/191 [==============================] - 0s 68us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 101/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 102/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 103/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 104/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 105/400 191/191 [==============================] - 0s 73us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 106/400 191/191 [==============================] - 0s 78us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 107/400 191/191 [==============================] - 0s 73us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00107: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 108/400 191/191 [==============================] - 0s 78us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 109/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 110/400 191/191 [==============================] - 0s 52us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 111/400 191/191 [==============================] - 0s 84us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 112/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 113/400 191/191 [==============================] - 0s 63us/step - loss: 0.5050 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 114/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 115/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 116/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 117/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00117: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 118/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 119/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 120/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 121/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 122/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 123/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 124/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 125/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 126/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 127/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00127: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 128/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 129/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 130/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 131/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 132/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 133/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 134/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 135/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 136/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 137/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00137: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 138/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 139/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 140/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 141/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 142/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 143/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 144/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 145/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 146/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 147/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00147: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 148/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 149/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 150/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 151/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 152/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 153/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 154/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 155/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 156/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 157/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00157: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 158/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 159/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 160/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 161/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 162/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 163/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 164/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 165/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 166/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 167/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00167: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 168/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 169/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 170/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 171/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 172/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 173/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 174/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 175/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 176/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 177/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00177: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 178/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 179/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 180/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 181/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 182/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 183/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 184/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 185/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 186/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 187/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00187: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 188/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 189/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 190/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 191/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 192/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 193/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 194/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 195/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 196/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 197/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00197: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 198/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 199/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 200/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 201/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 202/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 203/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 204/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 205/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 206/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 207/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00207: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 208/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 209/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 210/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 211/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 212/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 213/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 214/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 215/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 216/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 217/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00217: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 218/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 219/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 220/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 221/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 222/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 223/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 224/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 225/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 226/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 227/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00227: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 228/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 229/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 230/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 231/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 232/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 233/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 234/400 191/191 [==============================] - 0s 32us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 235/400 191/191 [==============================] - 0s 110us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 236/400 191/191 [==============================] - 0s 47us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 237/400 191/191 [==============================] - 0s 47us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00237: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 238/400 191/191 [==============================] - 0s 37us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 239/400 191/191 [==============================] - 0s 82us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 240/400 191/191 [==============================] - 0s 0us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 241/400 191/191 [==============================] - 0s 137us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 242/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 243/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 244/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 245/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 246/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 247/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00247: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 248/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 249/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 250/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 251/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 252/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 253/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 254/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 255/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 256/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 257/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00257: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 258/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 259/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 260/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 261/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 262/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 263/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 264/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 265/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 266/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 267/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00267: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 268/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 269/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 270/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 271/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 272/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 273/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 274/400 191/191 [==============================] - 0s 58us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 275/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 276/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 277/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00277: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 278/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 279/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 280/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 281/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 282/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 283/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 284/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 285/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 286/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 287/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00287: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 288/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 289/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 290/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 291/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 292/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 293/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 294/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 295/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 296/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 297/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00297: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 298/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 299/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 300/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 301/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 302/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 303/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 304/400 191/191 [==============================] - 0s 63us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 305/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 306/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 307/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00307: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 308/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 309/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 310/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 311/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 312/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 313/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 314/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 315/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 316/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 317/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00317: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 318/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 319/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 320/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 321/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 322/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 323/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 324/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 325/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 326/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 327/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00327: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 328/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 329/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 330/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 331/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 332/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 333/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 334/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 335/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 336/400 191/191 [==============================] - 0s 110us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 337/400 191/191 [==============================] - 0s 105us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00337: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 338/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 339/400 191/191 [==============================] - 0s 126us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 340/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 341/400 191/191 [==============================] - 0s 105us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 342/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 343/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 344/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 345/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 346/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 347/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00347: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 348/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 349/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 350/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 351/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 352/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 353/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 354/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 355/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 356/400 191/191 [==============================] - 0s 110us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 357/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00357: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 358/400 191/191 [==============================] - 0s 115us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 359/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 360/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 361/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 362/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 363/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 364/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 365/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 366/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 367/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00367: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 368/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 369/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 370/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 371/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 372/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 373/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 374/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 375/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 376/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 377/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00377: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 378/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 379/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 380/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 381/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 382/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 383/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 384/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 385/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 386/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 387/400 191/191 [==============================] - 0s 94us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00387: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 388/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 389/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 390/400 191/191 [==============================] - 0s 84us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 391/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 392/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 393/400 191/191 [==============================] - 0s 99us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 394/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 395/400 191/191 [==============================] - 0s 89us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 396/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 397/400 191/191 [==============================] - 0s 68us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 00397: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 398/400 191/191 [==============================] - 0s 78us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 399/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719 Epoch 400/400 191/191 [==============================] - 0s 73us/step - loss: 0.5049 - accuracy: 0.7487 - val_loss: 0.6847 - val_accuracy: 0.6719
In [337]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 400)
In [338]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 47us/step test loss: 0.6846858859062195, test accuracy: 0.671875
In [339]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.4842105263157894
In [340]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.08943089430894313
KMeans¶
In [243]:
X
Out[243]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 0.898091 | 0.151819 | -1.172713 | 0.474387 | -0.020230 | 1.228657 |
| 1 | 0.618513 | -0.762588 | 0.061946 | 0.944076 | 0.697880 | 0.021150 |
| 2 | 0.685649 | 0.002933 | 0.719805 | -1.251700 | -0.952424 | 1.444556 |
| 3 | 1.175209 | -0.552349 | 0.336427 | 0.482978 | -0.212146 | -0.144225 |
| 4 | 1.350337 | -1.407757 | 0.258917 | -0.523670 | 0.099306 | 1.706064 |
| 5 | 0.907564 | -1.769301 | 1.177857 | -0.869472 | 0.392594 | 0.385760 |
| 6 | -0.071420 | -0.800769 | 0.238726 | 1.318866 | -1.075628 | -0.545006 |
| 7 | 0.476433 | -1.202140 | -1.713665 | 0.379487 | -0.347674 | 0.777899 |
| 8 | 0.572039 | -1.488738 | -0.403914 | -1.066061 | -0.818836 | 0.339231 |
| 9 | 0.741137 | 0.139987 | 0.726307 | 1.670135 | -0.317435 | -1.091941 |
| 10 | 0.533655 | -0.111619 | 0.435253 | 1.832919 | -0.556933 | -1.014603 |
| 11 | -0.667308 | 0.502566 | -1.137726 | -0.714521 | -0.497571 | 0.123297 |
| 12 | 0.161812 | 0.294263 | 0.659166 | -0.336211 | 1.410350 | -0.272418 |
| 13 | -0.373777 | -1.439681 | 0.009190 | 0.731635 | 0.138615 | 0.850511 |
| 14 | 0.745550 | 0.214669 | 0.209787 | 0.424963 | 0.448908 | -0.204578 |
| 15 | 0.320726 | 0.108060 | 0.208510 | -1.138882 | -0.874041 | -1.779091 |
| 16 | 0.646392 | -0.726119 | 0.153724 | -0.203580 | -1.017329 | -1.068601 |
| 17 | -0.042981 | -0.672256 | 0.358250 | -0.385808 | -0.341018 | -1.823744 |
| 18 | 0.822192 | 0.184879 | 1.658679 | 1.705929 | 3.070140 | -1.218005 |
| 19 | 0.175070 | 0.195153 | 1.969940 | 0.005043 | 0.430538 | -1.502715 |
| 20 | 1.339692 | -1.202498 | 0.487937 | -0.769520 | -1.973308 | -0.400699 |
| 21 | 1.290923 | -0.546138 | 0.120024 | 0.429258 | -0.165681 | 0.856938 |
| 22 | 1.528224 | -0.912727 | 0.962682 | -0.386673 | -0.772181 | -0.291766 |
| 23 | -0.486779 | -1.124424 | 0.559106 | 0.746533 | -1.101240 | 1.082216 |
| 24 | -0.230729 | 0.999926 | -0.678209 | -0.175670 | 1.412258 | 0.572372 |
| 25 | -0.632681 | 0.618852 | -0.778803 | -0.808112 | -0.442115 | -0.146177 |
| 26 | -1.151505 | -1.127449 | 1.500641 | -0.822825 | 0.158380 | 0.792656 |
| 27 | 0.265739 | -3.078847 | -0.939567 | 0.268673 | -0.642098 | -0.984495 |
| 28 | 0.623357 | -1.241561 | -1.149654 | 1.231993 | 2.023015 | -0.070476 |
| 29 | 0.930863 | -1.763587 | -1.608926 | 0.462097 | -0.677599 | -0.693427 |
| ... | ... | ... | ... | ... | ... | ... |
| 225 | -1.444140 | -0.088370 | -0.458428 | 0.530251 | -0.475625 | -0.057486 |
| 226 | -0.297006 | 0.887935 | 0.467148 | 2.000374 | -0.396849 | -0.846195 |
| 227 | -1.624166 | 0.777486 | 0.635044 | -1.376180 | 0.998008 | -0.910882 |
| 228 | 0.230618 | 1.438780 | 0.301556 | -1.353873 | -0.586627 | -0.102947 |
| 229 | -0.163123 | 1.329205 | 0.721279 | -1.383030 | 0.540446 | -1.181571 |
| 230 | -1.337576 | 0.249897 | 0.081067 | 0.886335 | -0.078090 | -0.344245 |
| 231 | 0.304553 | 0.584052 | 0.915910 | 2.455180 | 1.007231 | 0.268298 |
| 232 | -0.291785 | 0.247731 | -0.740382 | 0.896773 | 0.457951 | 0.390640 |
| 233 | -0.532056 | 1.686101 | 0.358185 | -1.561985 | 0.911246 | 0.638759 |
| 234 | -1.223692 | 0.723005 | 0.599197 | -0.955626 | 0.653814 | 0.112686 |
| 235 | 1.412552 | -0.817418 | 0.038464 | -2.397710 | -2.903923 | 1.454325 |
| 236 | 0.141392 | -0.756740 | -1.981390 | -0.636588 | 0.230786 | 0.968907 |
| 237 | 1.157567 | -0.442417 | -1.342532 | -0.893118 | -0.552517 | -0.791388 |
| 238 | -1.683225 | -0.036571 | 0.297162 | -1.488549 | 1.387872 | -0.306946 |
| 239 | -0.997159 | 0.655257 | 2.239993 | -1.422875 | 0.373101 | 0.159004 |
| 240 | -1.142741 | 0.931927 | 1.440876 | 0.665641 | -0.994237 | -1.093039 |
| 241 | -0.151675 | -0.971306 | 0.447819 | 0.895444 | -0.863907 | 0.150120 |
| 242 | -0.837654 | -1.170592 | 0.622658 | 0.448216 | -0.830715 | -0.222067 |
| 243 | -0.059101 | -0.857751 | 0.253657 | 0.272951 | -0.833270 | 0.160823 |
| 244 | 1.455210 | -1.123798 | 1.124970 | -1.841854 | -0.183521 | -0.193778 |
| 245 | 1.459407 | -1.071308 | -0.261053 | -0.731205 | 0.603463 | 0.358072 |
| 246 | 1.850117 | -1.364586 | 1.015519 | -1.479941 | -1.262489 | -0.485304 |
| 247 | 0.468703 | 0.776904 | -1.200084 | -0.109459 | 0.572206 | 0.353229 |
| 248 | 0.758187 | -0.030802 | -1.190930 | -0.092637 | 0.048267 | 2.174173 |
| 249 | 0.465492 | -0.042081 | 0.541343 | 0.584645 | 0.066443 | -1.886670 |
| 250 | -1.114193 | 1.666162 | 0.201458 | -1.543125 | -0.123758 | -0.430641 |
| 251 | -1.675129 | 1.101864 | 0.721966 | -1.964153 | 0.827116 | 0.134812 |
| 252 | -1.371728 | 0.888874 | -0.186673 | -0.931346 | 0.795500 | -1.063218 |
| 253 | 0.221249 | 0.272024 | -1.593712 | -0.242394 | 0.752955 | 1.102656 |
| 254 | -0.747040 | 1.308435 | 0.858494 | -1.950134 | 1.779312 | -0.711789 |
255 rows × 6 columns
In [244]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[244]:
[1530.0000000000002, 1266.8988304034983, 1085.4171102625123, 963.5827926636907, 872.5239995069635, 797.6140851961846, 747.1323294070899, 703.670300371115, 664.3614627122823, 637.5590430281768, 607.7011770650902, 585.4389967082509, 558.8506960652073, 540.5660329891642]
In [245]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[245]:
[<matplotlib.lines.Line2D at 0x1e82fedf908>]
K=3
In [248]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[248]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [249]:
kmeans_tc.labels_
Out[249]:
array([2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2,
0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 0, 0, 0,
0, 2, 2, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0,
1, 1, 1, 1, 1, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0, 1, 0, 1,
1, 1, 2, 2, 2, 0, 0, 0, 2, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 2, 1, 1,
1, 0, 1, 2, 0, 2, 2, 1, 1, 1, 2, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 0,
2, 2, 2, 2, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 2, 2, 2, 2, 1, 1,
1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2,
1, 2, 0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 2,
2, 2, 1, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 0, 2, 0, 2, 2, 0, 1, 1, 1, 2, 1])
In [250]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[250]:
array([2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 2,
0, 0, 1, 1, 0, 0, 0, 2, 2, 0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 0, 0, 0,
0, 2, 2, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0,
1, 1, 1, 1, 1, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 0, 2, 0, 1, 0, 1,
1, 1, 2, 2, 2, 0, 0, 0, 2, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 2, 1, 1,
1, 0, 1, 2, 0, 2, 2, 1, 1, 1, 2, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 0,
2, 2, 2, 2, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 2, 2, 2, 2, 1, 1,
1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 2,
1, 2, 0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 2,
2, 2, 1, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 1, 2, 2, 2, 1, 1, 1, 0,
0, 0, 0, 2, 0, 2, 2, 0, 1, 1, 1, 2, 1])
In [251]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [252]:
X
Out[252]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.898091 | 0.151819 | -1.172713 | 0.474387 | -0.020230 | 1.228657 | 2 | 0 |
| 1 | 0.618513 | -0.762588 | 0.061946 | 0.944076 | 0.697880 | 0.021150 | 0 | 0 |
| 2 | 0.685649 | 0.002933 | 0.719805 | -1.251700 | -0.952424 | 1.444556 | 2 | 0 |
| 3 | 1.175209 | -0.552349 | 0.336427 | 0.482978 | -0.212146 | -0.144225 | 0 | 0 |
| 4 | 1.350337 | -1.407757 | 0.258917 | -0.523670 | 0.099306 | 1.706064 | 2 | 0 |
| 5 | 0.907564 | -1.769301 | 1.177857 | -0.869472 | 0.392594 | 0.385760 | 0 | 0 |
| 6 | -0.071420 | -0.800769 | 0.238726 | 1.318866 | -1.075628 | -0.545006 | 0 | 0 |
| 7 | 0.476433 | -1.202140 | -1.713665 | 0.379487 | -0.347674 | 0.777899 | 2 | 0 |
| 8 | 0.572039 | -1.488738 | -0.403914 | -1.066061 | -0.818836 | 0.339231 | 2 | 0 |
| 9 | 0.741137 | 0.139987 | 0.726307 | 1.670135 | -0.317435 | -1.091941 | 0 | 0 |
| 10 | 0.533655 | -0.111619 | 0.435253 | 1.832919 | -0.556933 | -1.014603 | 0 | 0 |
| 11 | -0.667308 | 0.502566 | -1.137726 | -0.714521 | -0.497571 | 0.123297 | 2 | 0 |
| 12 | 0.161812 | 0.294263 | 0.659166 | -0.336211 | 1.410350 | -0.272418 | 1 | 0 |
| 13 | -0.373777 | -1.439681 | 0.009190 | 0.731635 | 0.138615 | 0.850511 | 0 | 0 |
| 14 | 0.745550 | 0.214669 | 0.209787 | 0.424963 | 0.448908 | -0.204578 | 0 | 0 |
| 15 | 0.320726 | 0.108060 | 0.208510 | -1.138882 | -0.874041 | -1.779091 | 1 | 0 |
| 16 | 0.646392 | -0.726119 | 0.153724 | -0.203580 | -1.017329 | -1.068601 | 0 | 0 |
| 17 | -0.042981 | -0.672256 | 0.358250 | -0.385808 | -0.341018 | -1.823744 | 0 | 0 |
| 18 | 0.822192 | 0.184879 | 1.658679 | 1.705929 | 3.070140 | -1.218005 | 0 | 0 |
| 19 | 0.175070 | 0.195153 | 1.969940 | 0.005043 | 0.430538 | -1.502715 | 0 | 0 |
| 20 | 1.339692 | -1.202498 | 0.487937 | -0.769520 | -1.973308 | -0.400699 | 0 | 0 |
| 21 | 1.290923 | -0.546138 | 0.120024 | 0.429258 | -0.165681 | 0.856938 | 2 | 0 |
| 22 | 1.528224 | -0.912727 | 0.962682 | -0.386673 | -0.772181 | -0.291766 | 0 | 0 |
| 23 | -0.486779 | -1.124424 | 0.559106 | 0.746533 | -1.101240 | 1.082216 | 0 | 0 |
| 24 | -0.230729 | 0.999926 | -0.678209 | -0.175670 | 1.412258 | 0.572372 | 1 | 0 |
| 25 | -0.632681 | 0.618852 | -0.778803 | -0.808112 | -0.442115 | -0.146177 | 1 | 0 |
| 26 | -1.151505 | -1.127449 | 1.500641 | -0.822825 | 0.158380 | 0.792656 | 0 | 0 |
| 27 | 0.265739 | -3.078847 | -0.939567 | 0.268673 | -0.642098 | -0.984495 | 0 | 0 |
| 28 | 0.623357 | -1.241561 | -1.149654 | 1.231993 | 2.023015 | -0.070476 | 0 | 0 |
| 29 | 0.930863 | -1.763587 | -1.608926 | 0.462097 | -0.677599 | -0.693427 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -1.444140 | -0.088370 | -0.458428 | 0.530251 | -0.475625 | -0.057486 | 1 | 1 |
| 226 | -0.297006 | 0.887935 | 0.467148 | 2.000374 | -0.396849 | -0.846195 | 0 | 1 |
| 227 | -1.624166 | 0.777486 | 0.635044 | -1.376180 | 0.998008 | -0.910882 | 1 | 1 |
| 228 | 0.230618 | 1.438780 | 0.301556 | -1.353873 | -0.586627 | -0.102947 | 1 | 1 |
| 229 | -0.163123 | 1.329205 | 0.721279 | -1.383030 | 0.540446 | -1.181571 | 1 | 1 |
| 230 | -1.337576 | 0.249897 | 0.081067 | 0.886335 | -0.078090 | -0.344245 | 1 | 1 |
| 231 | 0.304553 | 0.584052 | 0.915910 | 2.455180 | 1.007231 | 0.268298 | 0 | 1 |
| 232 | -0.291785 | 0.247731 | -0.740382 | 0.896773 | 0.457951 | 0.390640 | 2 | 1 |
| 233 | -0.532056 | 1.686101 | 0.358185 | -1.561985 | 0.911246 | 0.638759 | 1 | 1 |
| 234 | -1.223692 | 0.723005 | 0.599197 | -0.955626 | 0.653814 | 0.112686 | 1 | 1 |
| 235 | 1.412552 | -0.817418 | 0.038464 | -2.397710 | -2.903923 | 1.454325 | 2 | 1 |
| 236 | 0.141392 | -0.756740 | -1.981390 | -0.636588 | 0.230786 | 0.968907 | 2 | 1 |
| 237 | 1.157567 | -0.442417 | -1.342532 | -0.893118 | -0.552517 | -0.791388 | 2 | 1 |
| 238 | -1.683225 | -0.036571 | 0.297162 | -1.488549 | 1.387872 | -0.306946 | 1 | 1 |
| 239 | -0.997159 | 0.655257 | 2.239993 | -1.422875 | 0.373101 | 0.159004 | 1 | 1 |
| 240 | -1.142741 | 0.931927 | 1.440876 | 0.665641 | -0.994237 | -1.093039 | 1 | 1 |
| 241 | -0.151675 | -0.971306 | 0.447819 | 0.895444 | -0.863907 | 0.150120 | 0 | 1 |
| 242 | -0.837654 | -1.170592 | 0.622658 | 0.448216 | -0.830715 | -0.222067 | 0 | 1 |
| 243 | -0.059101 | -0.857751 | 0.253657 | 0.272951 | -0.833270 | 0.160823 | 0 | 1 |
| 244 | 1.455210 | -1.123798 | 1.124970 | -1.841854 | -0.183521 | -0.193778 | 0 | 1 |
| 245 | 1.459407 | -1.071308 | -0.261053 | -0.731205 | 0.603463 | 0.358072 | 2 | 1 |
| 246 | 1.850117 | -1.364586 | 1.015519 | -1.479941 | -1.262489 | -0.485304 | 0 | 1 |
| 247 | 0.468703 | 0.776904 | -1.200084 | -0.109459 | 0.572206 | 0.353229 | 2 | 1 |
| 248 | 0.758187 | -0.030802 | -1.190930 | -0.092637 | 0.048267 | 2.174173 | 2 | 1 |
| 249 | 0.465492 | -0.042081 | 0.541343 | 0.584645 | 0.066443 | -1.886670 | 0 | 1 |
| 250 | -1.114193 | 1.666162 | 0.201458 | -1.543125 | -0.123758 | -0.430641 | 1 | 1 |
| 251 | -1.675129 | 1.101864 | 0.721966 | -1.964153 | 0.827116 | 0.134812 | 1 | 1 |
| 252 | -1.371728 | 0.888874 | -0.186673 | -0.931346 | 0.795500 | -1.063218 | 1 | 1 |
| 253 | 0.221249 | 0.272024 | -1.593712 | -0.242394 | 0.752955 | 1.102656 | 2 | 1 |
| 254 | -0.747040 | 1.308435 | 0.858494 | -1.950134 | 1.779312 | -0.711789 | 1 | 1 |
255 rows × 8 columns
In [253]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[253]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e8304ba3c8>
In [75]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [341]:
X = df_n_ps_std_tc[2]
In [342]:
y = df_n_ps[2]['chosen']
In [343]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [344]:
X_train.shape
Out[344]:
(231, 6)
In [260]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [261]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [262]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [263]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.01, 'max_iter': 10}, que permiten obtener un Accuracy de 81.82% y un Kappa del 6.27
Tiempo total: 28.04 minutos
In [345]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.01
epochs = 10
In [346]:
input_tensor = Input(shape = (n0,))
In [347]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [348]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [349]:
model.summary()
Model: "model_17" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_17 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_55 (Dense) (None, 10) 70 _________________________________________________________________ dense_56 (Dense) (None, 1) 11 ================================================================= Total params: 81 Trainable params: 81 Non-trainable params: 0 _________________________________________________________________
In [350]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 231 samples, validate on 78 samples Epoch 1/10 231/231 [==============================] - 0s 632us/step - loss: 0.7179 - accuracy: 0.5281 - val_loss: 0.6663 - val_accuracy: 0.6026 Epoch 2/10 231/231 [==============================] - 0s 52us/step - loss: 0.6066 - accuracy: 0.7013 - val_loss: 0.6283 - val_accuracy: 0.6667 Epoch 3/10 231/231 [==============================] - 0s 56us/step - loss: 0.5464 - accuracy: 0.7576 - val_loss: 0.6155 - val_accuracy: 0.6538 Epoch 4/10 231/231 [==============================] - 0s 56us/step - loss: 0.5144 - accuracy: 0.7706 - val_loss: 0.5962 - val_accuracy: 0.7308 Epoch 5/10 231/231 [==============================] - 0s 56us/step - loss: 0.4943 - accuracy: 0.8009 - val_loss: 0.5916 - val_accuracy: 0.7436 Epoch 6/10 231/231 [==============================] - 0s 56us/step - loss: 0.4849 - accuracy: 0.8095 - val_loss: 0.5781 - val_accuracy: 0.7436 Epoch 7/10 231/231 [==============================] - 0s 56us/step - loss: 0.4773 - accuracy: 0.8052 - val_loss: 0.5673 - val_accuracy: 0.7436 Epoch 8/10 231/231 [==============================] - 0s 56us/step - loss: 0.4764 - accuracy: 0.8052 - val_loss: 0.5616 - val_accuracy: 0.7692 Epoch 9/10 231/231 [==============================] - 0s 52us/step - loss: 0.4757 - accuracy: 0.8052 - val_loss: 0.5632 - val_accuracy: 0.7692 Epoch 10/10 231/231 [==============================] - 0s 56us/step - loss: 0.4710 - accuracy: 0.8052 - val_loss: 0.5628 - val_accuracy: 0.7692
In [351]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 10)
In [352]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
78/78 [==============================] - 0s 51us/step test loss: 0.5627795901053991, test accuracy: 0.7692307829856873
In [353]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.5194444444444444
In [354]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.05645161290322598
KMeans¶
In [274]:
X
Out[274]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 0.609365 | 0.535135 | 0.096066 | 1.915075 | 0.555249 | 1.854023 |
| 1 | -0.157519 | -1.311578 | -0.486252 | 0.435334 | -0.648735 | -1.184658 |
| 2 | -1.148976 | -1.325889 | 0.573178 | -1.556913 | 0.331644 | 1.236576 |
| 3 | -0.996141 | -0.557658 | 0.998693 | -0.200592 | -0.357882 | 0.086757 |
| 4 | -0.648977 | -0.248473 | 0.461357 | -1.268368 | -1.468590 | -0.111563 |
| 5 | 0.642800 | -1.165140 | -1.792767 | -0.560937 | -0.122789 | 1.152255 |
| 6 | 0.443136 | -0.482419 | -1.016712 | 0.352897 | -0.127270 | 0.658601 |
| 7 | -0.022060 | -0.811084 | -0.405764 | 0.350011 | 0.381710 | -0.262666 |
| 8 | 0.961630 | 1.407288 | 1.551164 | 0.606159 | -0.391772 | 0.029812 |
| 9 | 0.798279 | 1.109447 | -0.137057 | 0.704421 | -0.893816 | 1.290122 |
| 10 | 0.685212 | 1.076167 | -0.288224 | 1.508940 | 0.286089 | 2.439563 |
| 11 | 0.803628 | 1.560996 | 0.774426 | -0.123135 | -0.047389 | -0.441250 |
| 12 | 0.099890 | 1.445746 | -1.453815 | -0.214603 | -1.139872 | -0.252335 |
| 13 | -0.212854 | 1.286504 | 0.720036 | -0.872519 | 0.955706 | -2.078957 |
| 14 | -1.537501 | -0.213678 | -1.834954 | -1.070992 | 1.539047 | 0.982703 |
| 15 | -1.440013 | 0.872943 | -0.809520 | -0.147752 | 0.222384 | -1.045942 |
| 16 | -1.697309 | 0.606859 | -1.747229 | 0.351080 | 1.440681 | -1.505620 |
| 17 | 1.508685 | 0.904715 | 2.767009 | 0.452322 | 0.416081 | -1.050028 |
| 18 | 0.675946 | 0.502807 | 1.115627 | -1.151104 | 0.856946 | -1.406132 |
| 19 | 1.299159 | 0.428687 | 1.764048 | 2.076242 | 0.575092 | 0.580747 |
| 20 | 0.769693 | -0.740825 | 0.452717 | -0.142755 | 0.170817 | -0.358021 |
| 21 | 0.495856 | -0.499875 | -0.034482 | 0.350727 | 0.157251 | 0.626608 |
| 22 | 0.772290 | -0.678641 | 0.547946 | -0.428025 | -1.433904 | -0.355505 |
| 23 | 0.137654 | 1.496537 | 0.060579 | -0.502941 | 0.411442 | -0.122067 |
| 24 | 0.347129 | 0.809472 | 0.948516 | -0.022364 | -0.154971 | -0.309935 |
| 25 | 0.029111 | 1.842864 | 0.123391 | 0.049825 | 0.617290 | -0.085588 |
| 26 | -1.473729 | 0.531152 | 0.801294 | -1.410029 | 1.941482 | 0.485707 |
| 27 | -0.484210 | 0.210847 | 1.009613 | 1.253693 | -0.492018 | -0.245022 |
| 28 | -0.013618 | -2.024383 | -0.832429 | 0.712753 | 0.584767 | 0.136182 |
| 29 | -1.746529 | 0.488857 | -0.774537 | 0.083168 | 1.702499 | -0.373082 |
| ... | ... | ... | ... | ... | ... | ... |
| 279 | -0.218279 | -0.903841 | -1.454712 | 1.847673 | -0.519253 | -0.262567 |
| 280 | 0.264235 | -1.579208 | -1.404331 | -0.267295 | 1.169932 | 0.986186 |
| 281 | -0.895563 | 0.461466 | 0.497480 | 0.976976 | -0.096074 | 0.123883 |
| 282 | -1.010758 | 0.423694 | 0.383226 | 0.839004 | -0.068742 | -0.327768 |
| 283 | -1.300227 | 0.920815 | 0.844807 | 0.541693 | 0.080564 | -0.199530 |
| 284 | 1.450169 | 0.335733 | 1.585783 | 0.110995 | 1.150821 | -0.583010 |
| 285 | 1.913390 | 0.883060 | 1.899591 | 1.145796 | 0.539016 | 0.768216 |
| 286 | 1.444559 | 0.481538 | 1.742822 | 0.581765 | 1.145564 | -0.912228 |
| 287 | 1.470621 | -0.218928 | -0.057911 | -0.985989 | -1.598797 | -0.641710 |
| 288 | 1.040063 | -0.416007 | 0.629628 | -0.616364 | -1.267930 | -0.730043 |
| 289 | 1.328762 | -0.491263 | 0.742595 | -0.863279 | -1.402041 | -0.885662 |
| 290 | 0.397008 | 1.213991 | -0.429044 | 0.832112 | -0.843278 | 1.390832 |
| 291 | 0.172503 | 0.897500 | -0.272973 | 0.210231 | -0.458942 | 0.381469 |
| 292 | 0.661186 | 1.163101 | -0.294020 | -0.024527 | -0.744258 | 0.845600 |
| 293 | -0.773212 | -0.895468 | 0.318110 | 1.184648 | -0.212121 | -0.353155 |
| 294 | -0.370777 | -1.414554 | -1.094557 | 0.703279 | 1.214849 | 0.177043 |
| 295 | 0.138250 | -1.936963 | 0.151220 | 0.398994 | 2.119363 | 1.534181 |
| 296 | 0.819776 | -0.472427 | -0.322556 | 0.733590 | 0.794066 | 0.850059 |
| 297 | 0.754438 | -1.235411 | 0.322527 | -0.225499 | -1.850620 | 0.965915 |
| 298 | 1.007225 | -0.846471 | -0.431575 | -0.067897 | 0.376757 | 0.841471 |
| 299 | 0.218592 | -0.968903 | -0.899045 | -0.447626 | -0.181640 | -0.029332 |
| 300 | 0.664524 | -0.896436 | -1.249763 | 0.108770 | 0.071596 | -0.691963 |
| 301 | 0.711838 | -1.301487 | -0.735548 | -0.243133 | 0.655501 | 0.656529 |
| 302 | -0.800747 | 0.870240 | 0.930120 | 1.819532 | 0.060854 | 0.468146 |
| 303 | -1.334960 | 1.396005 | 2.690760 | 1.279657 | 0.956382 | 1.160282 |
| 304 | -0.792172 | 0.906439 | 1.374725 | 1.647470 | -0.613395 | 0.102004 |
| 305 | -1.381202 | 0.689863 | -0.347752 | 0.607044 | -0.309056 | -0.227433 |
| 306 | -1.296109 | 0.634783 | -0.484683 | 0.814045 | -0.809678 | 0.515808 |
| 307 | 0.326809 | -0.987801 | -1.740993 | 0.307094 | 2.260097 | 0.394211 |
| 308 | 0.873341 | 2.041793 | -1.371451 | -2.438935 | -0.567199 | -1.300657 |
309 rows × 6 columns
In [275]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[275]:
[1854.0, 1536.8736642821204, 1324.7285075839484, 1167.9156349888185, 1059.6165149053068, 950.057768838126, 894.4360136638212, 823.7421512103674, 771.932101822877, 716.3847905620515, 681.7237006830833, 653.1513609666642, 638.9262100013641, 609.9795957680683]
In [276]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[276]:
[<matplotlib.lines.Line2D at 0x1e831b5c9e8>]
K=4
In [277]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[277]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [278]:
kmeans_tc.labels_
Out[278]:
array([2, 1, 3, 1, 3, 2, 2, 2, 1, 2, 2, 3, 1, 0, 0, 0, 0, 3, 0, 1, 3, 2,
3, 0, 1, 0, 0, 1, 2, 0, 2, 2, 2, 1, 0, 2, 2, 3, 1, 1, 1, 2, 2, 2,
2, 2, 1, 3, 3, 3, 1, 1, 1, 0, 0, 0, 2, 2, 3, 2, 0, 2, 0, 2, 2, 2,
3, 1, 1, 1, 1, 3, 3, 3, 0, 0, 2, 1, 1, 1, 2, 2, 2, 0, 1, 0, 3, 3,
3, 2, 0, 1, 0, 2, 1, 1, 1, 1, 2, 2, 2, 3, 0, 1, 1, 1, 3, 2, 0, 2,
0, 3, 3, 3, 2, 0, 0, 1, 0, 0, 3, 3, 0, 1, 1, 0, 3, 3, 2, 0, 2, 0,
0, 0, 3, 2, 1, 0, 0, 0, 3, 3, 1, 2, 3, 3, 0, 1, 0, 0, 3, 0, 1, 3,
2, 0, 3, 1, 3, 1, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 1, 1, 1, 3,
3, 2, 2, 2, 2, 2, 2, 3, 1, 0, 0, 1, 2, 1, 1, 0, 0, 2, 3, 3, 1, 0,
1, 0, 1, 2, 2, 2, 1, 0, 1, 3, 3, 3, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1,
1, 2, 2, 3, 1, 3, 3, 0, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 0, 0, 0, 1,
1, 3, 3, 1, 2, 3, 2, 3, 1, 3, 2, 0, 1, 2, 2, 2, 0, 1, 0, 3, 2, 3,
0, 0, 2, 0, 0, 0, 3, 2, 2, 2, 1, 1, 2, 1, 0, 2, 2, 1, 1, 1, 3, 3,
3, 3, 3, 3, 2, 1, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2,
0])
In [279]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[279]:
array([2, 1, 3, 1, 3, 2, 2, 2, 1, 2, 2, 3, 1, 0, 0, 0, 0, 3, 0, 1, 3, 2,
3, 0, 1, 0, 0, 1, 2, 0, 2, 2, 2, 1, 0, 2, 2, 3, 1, 1, 1, 2, 2, 2,
2, 2, 1, 3, 3, 3, 1, 1, 1, 0, 0, 0, 2, 2, 3, 2, 0, 2, 0, 2, 2, 2,
3, 1, 1, 1, 1, 3, 3, 3, 0, 0, 2, 1, 1, 1, 2, 2, 2, 0, 1, 0, 3, 3,
3, 2, 0, 1, 0, 2, 1, 1, 1, 1, 2, 2, 2, 3, 0, 1, 1, 1, 3, 2, 0, 2,
0, 3, 3, 3, 2, 0, 0, 1, 0, 0, 3, 3, 0, 1, 1, 0, 3, 3, 2, 0, 2, 0,
0, 0, 3, 2, 1, 0, 0, 0, 3, 3, 1, 2, 3, 3, 0, 1, 0, 0, 3, 0, 1, 3,
2, 0, 3, 1, 3, 1, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 1, 1, 1, 3,
3, 2, 2, 2, 2, 2, 2, 3, 1, 0, 0, 1, 2, 1, 1, 0, 0, 2, 3, 3, 1, 0,
1, 0, 1, 2, 2, 2, 1, 0, 1, 3, 3, 3, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1,
1, 2, 2, 3, 1, 3, 3, 0, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 0, 0, 0, 1,
1, 3, 3, 1, 2, 3, 2, 3, 1, 3, 2, 0, 1, 2, 2, 2, 0, 1, 0, 3, 2, 3,
0, 0, 2, 0, 0, 0, 3, 2, 2, 2, 1, 1, 2, 1, 0, 2, 2, 1, 1, 1, 3, 3,
3, 3, 3, 3, 2, 1, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2,
0])
In [280]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [281]:
X
Out[281]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.609365 | 0.535135 | 0.096066 | 1.915075 | 0.555249 | 1.854023 | 2 | 0 |
| 1 | -0.157519 | -1.311578 | -0.486252 | 0.435334 | -0.648735 | -1.184658 | 1 | 0 |
| 2 | -1.148976 | -1.325889 | 0.573178 | -1.556913 | 0.331644 | 1.236576 | 3 | 0 |
| 3 | -0.996141 | -0.557658 | 0.998693 | -0.200592 | -0.357882 | 0.086757 | 1 | 0 |
| 4 | -0.648977 | -0.248473 | 0.461357 | -1.268368 | -1.468590 | -0.111563 | 3 | 0 |
| 5 | 0.642800 | -1.165140 | -1.792767 | -0.560937 | -0.122789 | 1.152255 | 2 | 0 |
| 6 | 0.443136 | -0.482419 | -1.016712 | 0.352897 | -0.127270 | 0.658601 | 2 | 0 |
| 7 | -0.022060 | -0.811084 | -0.405764 | 0.350011 | 0.381710 | -0.262666 | 2 | 0 |
| 8 | 0.961630 | 1.407288 | 1.551164 | 0.606159 | -0.391772 | 0.029812 | 1 | 0 |
| 9 | 0.798279 | 1.109447 | -0.137057 | 0.704421 | -0.893816 | 1.290122 | 2 | 0 |
| 10 | 0.685212 | 1.076167 | -0.288224 | 1.508940 | 0.286089 | 2.439563 | 2 | 0 |
| 11 | 0.803628 | 1.560996 | 0.774426 | -0.123135 | -0.047389 | -0.441250 | 3 | 0 |
| 12 | 0.099890 | 1.445746 | -1.453815 | -0.214603 | -1.139872 | -0.252335 | 1 | 0 |
| 13 | -0.212854 | 1.286504 | 0.720036 | -0.872519 | 0.955706 | -2.078957 | 0 | 0 |
| 14 | -1.537501 | -0.213678 | -1.834954 | -1.070992 | 1.539047 | 0.982703 | 0 | 0 |
| 15 | -1.440013 | 0.872943 | -0.809520 | -0.147752 | 0.222384 | -1.045942 | 0 | 0 |
| 16 | -1.697309 | 0.606859 | -1.747229 | 0.351080 | 1.440681 | -1.505620 | 0 | 0 |
| 17 | 1.508685 | 0.904715 | 2.767009 | 0.452322 | 0.416081 | -1.050028 | 3 | 0 |
| 18 | 0.675946 | 0.502807 | 1.115627 | -1.151104 | 0.856946 | -1.406132 | 0 | 0 |
| 19 | 1.299159 | 0.428687 | 1.764048 | 2.076242 | 0.575092 | 0.580747 | 1 | 0 |
| 20 | 0.769693 | -0.740825 | 0.452717 | -0.142755 | 0.170817 | -0.358021 | 3 | 0 |
| 21 | 0.495856 | -0.499875 | -0.034482 | 0.350727 | 0.157251 | 0.626608 | 2 | 0 |
| 22 | 0.772290 | -0.678641 | 0.547946 | -0.428025 | -1.433904 | -0.355505 | 3 | 0 |
| 23 | 0.137654 | 1.496537 | 0.060579 | -0.502941 | 0.411442 | -0.122067 | 0 | 0 |
| 24 | 0.347129 | 0.809472 | 0.948516 | -0.022364 | -0.154971 | -0.309935 | 1 | 0 |
| 25 | 0.029111 | 1.842864 | 0.123391 | 0.049825 | 0.617290 | -0.085588 | 0 | 0 |
| 26 | -1.473729 | 0.531152 | 0.801294 | -1.410029 | 1.941482 | 0.485707 | 0 | 0 |
| 27 | -0.484210 | 0.210847 | 1.009613 | 1.253693 | -0.492018 | -0.245022 | 1 | 0 |
| 28 | -0.013618 | -2.024383 | -0.832429 | 0.712753 | 0.584767 | 0.136182 | 2 | 0 |
| 29 | -1.746529 | 0.488857 | -0.774537 | 0.083168 | 1.702499 | -0.373082 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 279 | -0.218279 | -0.903841 | -1.454712 | 1.847673 | -0.519253 | -0.262567 | 2 | 1 |
| 280 | 0.264235 | -1.579208 | -1.404331 | -0.267295 | 1.169932 | 0.986186 | 2 | 1 |
| 281 | -0.895563 | 0.461466 | 0.497480 | 0.976976 | -0.096074 | 0.123883 | 1 | 1 |
| 282 | -1.010758 | 0.423694 | 0.383226 | 0.839004 | -0.068742 | -0.327768 | 1 | 1 |
| 283 | -1.300227 | 0.920815 | 0.844807 | 0.541693 | 0.080564 | -0.199530 | 1 | 1 |
| 284 | 1.450169 | 0.335733 | 1.585783 | 0.110995 | 1.150821 | -0.583010 | 3 | 1 |
| 285 | 1.913390 | 0.883060 | 1.899591 | 1.145796 | 0.539016 | 0.768216 | 3 | 1 |
| 286 | 1.444559 | 0.481538 | 1.742822 | 0.581765 | 1.145564 | -0.912228 | 3 | 1 |
| 287 | 1.470621 | -0.218928 | -0.057911 | -0.985989 | -1.598797 | -0.641710 | 3 | 1 |
| 288 | 1.040063 | -0.416007 | 0.629628 | -0.616364 | -1.267930 | -0.730043 | 3 | 1 |
| 289 | 1.328762 | -0.491263 | 0.742595 | -0.863279 | -1.402041 | -0.885662 | 3 | 1 |
| 290 | 0.397008 | 1.213991 | -0.429044 | 0.832112 | -0.843278 | 1.390832 | 2 | 1 |
| 291 | 0.172503 | 0.897500 | -0.272973 | 0.210231 | -0.458942 | 0.381469 | 1 | 1 |
| 292 | 0.661186 | 1.163101 | -0.294020 | -0.024527 | -0.744258 | 0.845600 | 2 | 1 |
| 293 | -0.773212 | -0.895468 | 0.318110 | 1.184648 | -0.212121 | -0.353155 | 1 | 1 |
| 294 | -0.370777 | -1.414554 | -1.094557 | 0.703279 | 1.214849 | 0.177043 | 2 | 1 |
| 295 | 0.138250 | -1.936963 | 0.151220 | 0.398994 | 2.119363 | 1.534181 | 2 | 1 |
| 296 | 0.819776 | -0.472427 | -0.322556 | 0.733590 | 0.794066 | 0.850059 | 2 | 1 |
| 297 | 0.754438 | -1.235411 | 0.322527 | -0.225499 | -1.850620 | 0.965915 | 3 | 1 |
| 298 | 1.007225 | -0.846471 | -0.431575 | -0.067897 | 0.376757 | 0.841471 | 2 | 1 |
| 299 | 0.218592 | -0.968903 | -0.899045 | -0.447626 | -0.181640 | -0.029332 | 2 | 1 |
| 300 | 0.664524 | -0.896436 | -1.249763 | 0.108770 | 0.071596 | -0.691963 | 2 | 1 |
| 301 | 0.711838 | -1.301487 | -0.735548 | -0.243133 | 0.655501 | 0.656529 | 2 | 1 |
| 302 | -0.800747 | 0.870240 | 0.930120 | 1.819532 | 0.060854 | 0.468146 | 1 | 1 |
| 303 | -1.334960 | 1.396005 | 2.690760 | 1.279657 | 0.956382 | 1.160282 | 1 | 1 |
| 304 | -0.792172 | 0.906439 | 1.374725 | 1.647470 | -0.613395 | 0.102004 | 1 | 1 |
| 305 | -1.381202 | 0.689863 | -0.347752 | 0.607044 | -0.309056 | -0.227433 | 1 | 1 |
| 306 | -1.296109 | 0.634783 | -0.484683 | 0.814045 | -0.809678 | 0.515808 | 1 | 1 |
| 307 | 0.326809 | -0.987801 | -1.740993 | 0.307094 | 2.260097 | 0.394211 | 2 | 1 |
| 308 | 0.873341 | 2.041793 | -1.371451 | -2.438935 | -0.567199 | -1.300657 | 0 | 1 |
309 rows × 8 columns
In [282]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[282]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e831bdb940>
In [355]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [356]:
X = df_n_ps_std_tc[3]
In [357]:
y = df_n_ps[3]['chosen']
In [358]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [359]:
X_train.shape
Out[359]:
(139, 6)
In [288]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [289]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [290]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [291]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30,), 'learning_rate_init': 0.007, 'max_iter': 1000}, que permiten obtener un Accuracy de 80.58% y un Kappa del 50.08
Tiempo total: 22.27 minutos
In [360]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.007
epochs = 1000
In [361]:
input_tensor = Input(shape = (n0,))
In [362]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [363]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [364]:
model.summary()
Model: "model_18" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_18 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_57 (Dense) (None, 30) 210 _________________________________________________________________ dense_58 (Dense) (None, 1) 31 ================================================================= Total params: 241 Trainable params: 241 Non-trainable params: 0 _________________________________________________________________
In [365]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 139 samples, validate on 47 samples Epoch 1/1000 139/139 [==============================] - 0s 1ms/step - loss: 0.6991 - accuracy: 0.5396 - val_loss: 0.6750 - val_accuracy: 0.6596 Epoch 2/1000 139/139 [==============================] - 0s 72us/step - loss: 0.6502 - accuracy: 0.6331 - val_loss: 0.6494 - val_accuracy: 0.6596 Epoch 3/1000 139/139 [==============================] - 0s 72us/step - loss: 0.6211 - accuracy: 0.6691 - val_loss: 0.6343 - val_accuracy: 0.6809 Epoch 4/1000 139/139 [==============================] - 0s 79us/step - loss: 0.6115 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6809 Epoch 5/1000 139/139 [==============================] - 0s 65us/step - loss: 0.6039 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 6/1000 139/139 [==============================] - 0s 72us/step - loss: 0.6032 - accuracy: 0.6835 - val_loss: 0.6279 - val_accuracy: 0.6383 Epoch 7/1000 139/139 [==============================] - 0s 72us/step - loss: 0.6017 - accuracy: 0.6978 - val_loss: 0.6370 - val_accuracy: 0.6383 Epoch 8/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5974 - accuracy: 0.6978 - val_loss: 0.6361 - val_accuracy: 0.6383 Epoch 9/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5954 - accuracy: 0.6978 - val_loss: 0.6361 - val_accuracy: 0.6383 Epoch 10/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5932 - accuracy: 0.6978 - val_loss: 0.6377 - val_accuracy: 0.6383 Epoch 11/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5925 - accuracy: 0.6906 - val_loss: 0.6333 - val_accuracy: 0.6383 Epoch 12/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5886 - accuracy: 0.6906 - val_loss: 0.6396 - val_accuracy: 0.6383 Epoch 13/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5881 - accuracy: 0.6835 - val_loss: 0.6433 - val_accuracy: 0.6383 Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.0035000001080334187. Epoch 14/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5854 - accuracy: 0.6835 - val_loss: 0.6396 - val_accuracy: 0.6383 Epoch 15/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5840 - accuracy: 0.6835 - val_loss: 0.6392 - val_accuracy: 0.6383 Epoch 16/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5829 - accuracy: 0.6835 - val_loss: 0.6382 - val_accuracy: 0.6383 Epoch 17/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5809 - accuracy: 0.6835 - val_loss: 0.6360 - val_accuracy: 0.6383 Epoch 18/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5801 - accuracy: 0.6906 - val_loss: 0.6355 - val_accuracy: 0.6383 Epoch 19/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5783 - accuracy: 0.6906 - val_loss: 0.6337 - val_accuracy: 0.6383 Epoch 20/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5777 - accuracy: 0.6835 - val_loss: 0.6298 - val_accuracy: 0.6383 Epoch 21/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5765 - accuracy: 0.6906 - val_loss: 0.6288 - val_accuracy: 0.6383 Epoch 22/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5753 - accuracy: 0.6978 - val_loss: 0.6276 - val_accuracy: 0.6383 Epoch 23/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5739 - accuracy: 0.6978 - val_loss: 0.6261 - val_accuracy: 0.6383 Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.0017500000540167093. Epoch 24/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5725 - accuracy: 0.6978 - val_loss: 0.6265 - val_accuracy: 0.6383 Epoch 25/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5722 - accuracy: 0.6978 - val_loss: 0.6269 - val_accuracy: 0.6383 Epoch 26/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5717 - accuracy: 0.6978 - val_loss: 0.6275 - val_accuracy: 0.6383 Epoch 27/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5710 - accuracy: 0.6906 - val_loss: 0.6281 - val_accuracy: 0.6383 Epoch 28/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5711 - accuracy: 0.6835 - val_loss: 0.6284 - val_accuracy: 0.6383 Epoch 29/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5698 - accuracy: 0.6835 - val_loss: 0.6282 - val_accuracy: 0.6383 Epoch 30/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5696 - accuracy: 0.6906 - val_loss: 0.6289 - val_accuracy: 0.6383 Epoch 31/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5692 - accuracy: 0.6906 - val_loss: 0.6276 - val_accuracy: 0.6383 Epoch 32/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5681 - accuracy: 0.6906 - val_loss: 0.6276 - val_accuracy: 0.6383 Epoch 33/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5677 - accuracy: 0.6906 - val_loss: 0.6274 - val_accuracy: 0.6383 Epoch 00033: ReduceLROnPlateau reducing learning rate to 0.0008750000270083547. Epoch 34/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5669 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 35/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5664 - accuracy: 0.6906 - val_loss: 0.6274 - val_accuracy: 0.6383 Epoch 36/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5662 - accuracy: 0.6906 - val_loss: 0.6278 - val_accuracy: 0.6383 Epoch 37/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5659 - accuracy: 0.6906 - val_loss: 0.6281 - val_accuracy: 0.6383 Epoch 38/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5655 - accuracy: 0.6906 - val_loss: 0.6285 - val_accuracy: 0.6383 Epoch 39/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5652 - accuracy: 0.6906 - val_loss: 0.6287 - val_accuracy: 0.6383 Epoch 40/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5651 - accuracy: 0.6906 - val_loss: 0.6295 - val_accuracy: 0.6383 Epoch 41/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5645 - accuracy: 0.6906 - val_loss: 0.6295 - val_accuracy: 0.6383 Epoch 42/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5642 - accuracy: 0.6906 - val_loss: 0.6296 - val_accuracy: 0.6383 Epoch 43/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5641 - accuracy: 0.6906 - val_loss: 0.6298 - val_accuracy: 0.6383 Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.00043750001350417733. Epoch 44/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5635 - accuracy: 0.6906 - val_loss: 0.6296 - val_accuracy: 0.6383 Epoch 45/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5633 - accuracy: 0.6906 - val_loss: 0.6297 - val_accuracy: 0.6383 Epoch 46/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5632 - accuracy: 0.6835 - val_loss: 0.6298 - val_accuracy: 0.6383 Epoch 47/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5630 - accuracy: 0.6835 - val_loss: 0.6292 - val_accuracy: 0.6383 Epoch 48/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5630 - accuracy: 0.6906 - val_loss: 0.6285 - val_accuracy: 0.6383 Epoch 49/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5626 - accuracy: 0.6906 - val_loss: 0.6284 - val_accuracy: 0.6383 Epoch 50/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5625 - accuracy: 0.6906 - val_loss: 0.6280 - val_accuracy: 0.6383 Epoch 51/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5622 - accuracy: 0.6906 - val_loss: 0.6279 - val_accuracy: 0.6383 Epoch 52/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5621 - accuracy: 0.6906 - val_loss: 0.6282 - val_accuracy: 0.6383 Epoch 53/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5620 - accuracy: 0.6906 - val_loss: 0.6281 - val_accuracy: 0.6383 Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.00021875000675208867. Epoch 54/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5618 - accuracy: 0.6906 - val_loss: 0.6281 - val_accuracy: 0.6383 Epoch 55/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5617 - accuracy: 0.6906 - val_loss: 0.6281 - val_accuracy: 0.6383 Epoch 56/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5616 - accuracy: 0.6906 - val_loss: 0.6279 - val_accuracy: 0.6383 Epoch 57/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5616 - accuracy: 0.6906 - val_loss: 0.6277 - val_accuracy: 0.6383 Epoch 58/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5614 - accuracy: 0.6906 - val_loss: 0.6277 - val_accuracy: 0.6383 Epoch 59/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5613 - accuracy: 0.6906 - val_loss: 0.6278 - val_accuracy: 0.6383 Epoch 60/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5613 - accuracy: 0.6906 - val_loss: 0.6276 - val_accuracy: 0.6383 Epoch 61/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5612 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 62/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5611 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 63/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5610 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 00063: ReduceLROnPlateau reducing learning rate to 0.00010937500337604433. Epoch 64/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5609 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 65/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5609 - accuracy: 0.6906 - val_loss: 0.6274 - val_accuracy: 0.6383 Epoch 66/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5608 - accuracy: 0.6906 - val_loss: 0.6274 - val_accuracy: 0.6383 Epoch 67/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5608 - accuracy: 0.6906 - val_loss: 0.6275 - val_accuracy: 0.6383 Epoch 68/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5608 - accuracy: 0.6906 - val_loss: 0.6275 - val_accuracy: 0.6383 Epoch 69/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5607 - accuracy: 0.6906 - val_loss: 0.6275 - val_accuracy: 0.6383 Epoch 70/1000 139/139 [==============================] - ETA: 0s - loss: 0.5842 - accuracy: 0.68 - 0s 86us/step - loss: 0.5607 - accuracy: 0.6906 - val_loss: 0.6275 - val_accuracy: 0.6383 Epoch 71/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5606 - accuracy: 0.6906 - val_loss: 0.6274 - val_accuracy: 0.6383 Epoch 72/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5606 - accuracy: 0.6906 - val_loss: 0.6273 - val_accuracy: 0.6383 Epoch 73/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5605 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00073: ReduceLROnPlateau reducing learning rate to 5.4687501688022166e-05. Epoch 74/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5605 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 75/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5605 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 76/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5604 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 77/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5604 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 78/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5604 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 79/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5604 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 80/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5603 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 81/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5603 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 82/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5603 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 83/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5603 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00083: ReduceLROnPlateau reducing learning rate to 2.7343750844011083e-05. Epoch 84/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5603 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 85/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 86/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 87/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 88/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 89/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 90/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 91/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 92/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 93/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00093: ReduceLROnPlateau reducing learning rate to 1.3671875422005542e-05. Epoch 94/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5602 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 95/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 96/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 97/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 98/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 99/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 100/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 101/1000 139/139 [==============================] - ETA: 0s - loss: 0.5625 - accuracy: 0.68 - 0s 93us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 102/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 103/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00103: ReduceLROnPlateau reducing learning rate to 6.835937711002771e-06. Epoch 104/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 105/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 106/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 107/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 108/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 109/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 110/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 111/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 112/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 113/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00113: ReduceLROnPlateau reducing learning rate to 3.4179688555013854e-06. Epoch 114/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 115/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 116/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 117/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 118/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 119/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 120/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 121/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 122/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 123/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00123: ReduceLROnPlateau reducing learning rate to 1.7089844277506927e-06. Epoch 124/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 125/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 126/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 127/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 128/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 129/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 130/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5601 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 131/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 132/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 133/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00133: ReduceLROnPlateau reducing learning rate to 8.544922138753464e-07. Epoch 134/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 135/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 136/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 137/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 138/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 139/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 140/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 141/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 142/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 143/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00143: ReduceLROnPlateau reducing learning rate to 4.272461069376732e-07. Epoch 144/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 145/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 146/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 147/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 148/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 149/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 150/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 151/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 152/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 153/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00153: ReduceLROnPlateau reducing learning rate to 2.136230534688366e-07. Epoch 154/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 155/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 156/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 157/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 158/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 159/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 160/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 161/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 162/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 163/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00163: ReduceLROnPlateau reducing learning rate to 1.068115267344183e-07. Epoch 164/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 165/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 166/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 167/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 168/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 169/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 170/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 171/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 172/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 173/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00173: ReduceLROnPlateau reducing learning rate to 5.340576336720915e-08. Epoch 174/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 175/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 176/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 177/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 178/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 179/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 180/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 181/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 182/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 183/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00183: ReduceLROnPlateau reducing learning rate to 2.6702881683604573e-08. Epoch 184/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 185/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 186/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 187/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 188/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 189/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 190/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 191/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 192/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 193/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00193: ReduceLROnPlateau reducing learning rate to 1.3351440841802287e-08. Epoch 194/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 195/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 196/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 197/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 198/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 199/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 200/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 201/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 202/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 203/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00203: ReduceLROnPlateau reducing learning rate to 6.675720420901143e-09. Epoch 204/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 205/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 206/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 207/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 208/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 209/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 210/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 211/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 212/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 213/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00213: ReduceLROnPlateau reducing learning rate to 3.3378602104505717e-09. Epoch 214/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 215/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 216/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 217/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 218/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 219/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 220/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 221/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 222/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 223/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00223: ReduceLROnPlateau reducing learning rate to 1.6689301052252858e-09. Epoch 224/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 225/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 226/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 227/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 228/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 229/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 230/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 231/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 232/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 233/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00233: ReduceLROnPlateau reducing learning rate to 8.344650526126429e-10. Epoch 234/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 235/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 236/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 237/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 238/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 239/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 240/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 241/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 242/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 243/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00243: ReduceLROnPlateau reducing learning rate to 4.1723252630632146e-10. Epoch 244/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 245/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 246/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 247/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 248/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 249/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 250/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 251/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 252/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 253/1000 139/139 [==============================] - 0s 58us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00253: ReduceLROnPlateau reducing learning rate to 2.0861626315316073e-10. Epoch 254/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 255/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 256/1000 139/139 [==============================] - 0s 72us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 257/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 258/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 259/1000 139/139 [==============================] - 0s 65us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 260/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 261/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 262/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 263/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00263: ReduceLROnPlateau reducing learning rate to 1.0430813157658037e-10. Epoch 264/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 265/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 266/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 267/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 268/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 269/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 270/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 271/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 272/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 273/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00273: ReduceLROnPlateau reducing learning rate to 5.215406578829018e-11. Epoch 274/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 275/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 276/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 277/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 278/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 279/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 280/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 281/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 282/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 283/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00283: ReduceLROnPlateau reducing learning rate to 2.607703289414509e-11. Epoch 284/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 285/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 286/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 287/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 288/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 289/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 290/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 291/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 292/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 293/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00293: ReduceLROnPlateau reducing learning rate to 1.3038516447072546e-11. Epoch 294/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 295/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 296/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 297/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 298/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 299/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 300/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 301/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 302/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 303/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00303: ReduceLROnPlateau reducing learning rate to 6.519258223536273e-12. Epoch 304/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 305/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 306/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 307/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 308/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 309/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 310/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 311/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 312/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 313/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00313: ReduceLROnPlateau reducing learning rate to 3.2596291117681364e-12. Epoch 314/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 315/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 316/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 317/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 318/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 319/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 320/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 321/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 322/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 323/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00323: ReduceLROnPlateau reducing learning rate to 1.6298145558840682e-12. Epoch 324/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 325/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 326/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 327/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 328/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 329/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 330/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 331/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 332/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 333/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00333: ReduceLROnPlateau reducing learning rate to 8.149072779420341e-13. Epoch 334/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 335/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 336/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 337/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 338/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 339/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 340/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 341/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 342/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 343/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00343: ReduceLROnPlateau reducing learning rate to 4.0745363897101705e-13. Epoch 344/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 345/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 346/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 347/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 348/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 349/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 350/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 351/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 352/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 353/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00353: ReduceLROnPlateau reducing learning rate to 2.0372681948550853e-13. Epoch 354/1000 139/139 [==============================] - 0s 158us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 355/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 356/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 357/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 358/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 359/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 360/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 361/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 362/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 363/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00363: ReduceLROnPlateau reducing learning rate to 1.0186340974275426e-13. Epoch 364/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 365/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 366/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 367/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 368/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 369/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 370/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 371/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 372/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 373/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00373: ReduceLROnPlateau reducing learning rate to 5.093170487137713e-14. Epoch 374/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 375/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 376/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 377/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 378/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 379/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 380/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 381/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 382/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 383/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00383: ReduceLROnPlateau reducing learning rate to 2.5465852435688566e-14. Epoch 384/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 385/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 386/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 387/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 388/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 389/1000 139/139 [==============================] - ETA: 0s - loss: 0.6763 - accuracy: 0.56 - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 390/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 391/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 392/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 393/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00393: ReduceLROnPlateau reducing learning rate to 1.2732926217844283e-14. Epoch 394/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 395/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 396/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 397/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 398/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 399/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 400/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 401/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 402/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 403/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00403: ReduceLROnPlateau reducing learning rate to 6.3664631089221414e-15. Epoch 404/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 405/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 406/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 407/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 408/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 409/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 410/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 411/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 412/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 413/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00413: ReduceLROnPlateau reducing learning rate to 3.1832315544610707e-15. Epoch 414/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 415/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 416/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 417/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 418/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 419/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 420/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 421/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 422/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 423/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00423: ReduceLROnPlateau reducing learning rate to 1.5916157772305354e-15. Epoch 424/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 425/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 426/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 427/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 428/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 429/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 430/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 431/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 432/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 433/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00433: ReduceLROnPlateau reducing learning rate to 7.958078886152677e-16. Epoch 434/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 435/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 436/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 437/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 438/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 439/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 440/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 441/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 442/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 443/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00443: ReduceLROnPlateau reducing learning rate to 3.9790394430763384e-16. Epoch 444/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 445/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 446/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 447/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 448/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 449/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 450/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 451/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 452/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 453/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00453: ReduceLROnPlateau reducing learning rate to 1.9895197215381692e-16. Epoch 454/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 455/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 456/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 457/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 458/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 459/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 460/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 461/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 462/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 463/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00463: ReduceLROnPlateau reducing learning rate to 9.947598607690846e-17. Epoch 464/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 465/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 466/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 467/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 468/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 469/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 470/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 471/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 472/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 473/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00473: ReduceLROnPlateau reducing learning rate to 4.973799303845423e-17. Epoch 474/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 475/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 476/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 477/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 478/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 479/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 480/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 481/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 482/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 483/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00483: ReduceLROnPlateau reducing learning rate to 2.4868996519227115e-17. Epoch 484/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 485/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 486/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 487/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 488/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 489/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 490/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 491/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 492/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 493/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00493: ReduceLROnPlateau reducing learning rate to 1.2434498259613557e-17. Epoch 494/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 495/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 496/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 497/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 498/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 499/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 500/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 501/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 502/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 503/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00503: ReduceLROnPlateau reducing learning rate to 6.217249129806779e-18. Epoch 504/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 505/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 506/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 507/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 508/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 509/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 510/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 511/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 512/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 513/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00513: ReduceLROnPlateau reducing learning rate to 3.1086245649033894e-18. Epoch 514/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 515/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 516/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 517/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 518/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 519/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 520/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 521/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 522/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 523/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00523: ReduceLROnPlateau reducing learning rate to 1.5543122824516947e-18. Epoch 524/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 525/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 526/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 527/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 528/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 529/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 530/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 531/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 532/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 533/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00533: ReduceLROnPlateau reducing learning rate to 7.771561412258473e-19. Epoch 534/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 535/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 536/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 537/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 538/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 539/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 540/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 541/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 542/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 543/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00543: ReduceLROnPlateau reducing learning rate to 3.8857807061292367e-19. Epoch 544/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 545/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 546/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 547/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 548/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 549/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 550/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 551/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 552/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 553/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00553: ReduceLROnPlateau reducing learning rate to 1.9428903530646184e-19. Epoch 554/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 555/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 556/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 557/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 558/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 559/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 560/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 561/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 562/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 563/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00563: ReduceLROnPlateau reducing learning rate to 9.714451765323092e-20. Epoch 564/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 565/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 566/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 567/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 568/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 569/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 570/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 571/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 572/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 573/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00573: ReduceLROnPlateau reducing learning rate to 4.857225882661546e-20. Epoch 574/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 575/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 576/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 577/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 578/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 579/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 580/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 581/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 582/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 583/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00583: ReduceLROnPlateau reducing learning rate to 2.428612941330773e-20. Epoch 584/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 585/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 586/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 587/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 588/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 589/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 590/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 591/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 592/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 593/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00593: ReduceLROnPlateau reducing learning rate to 1.2143064706653865e-20. Epoch 594/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 595/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 596/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 597/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 598/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 599/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 600/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 601/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 602/1000 139/139 [==============================] - 0s 158us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 603/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00603: ReduceLROnPlateau reducing learning rate to 6.071532353326932e-21. Epoch 604/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 605/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 606/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 607/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 608/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 609/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 610/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 611/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 612/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 613/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00613: ReduceLROnPlateau reducing learning rate to 3.035766176663466e-21. Epoch 614/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 615/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 616/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 617/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 618/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 619/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 620/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 621/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 622/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 623/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00623: ReduceLROnPlateau reducing learning rate to 1.517883088331733e-21. Epoch 624/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 625/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 626/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 627/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 628/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 629/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 630/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 631/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 632/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 633/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00633: ReduceLROnPlateau reducing learning rate to 7.589415441658665e-22. Epoch 634/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 635/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 636/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 637/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 638/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 639/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 640/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 641/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 642/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 643/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00643: ReduceLROnPlateau reducing learning rate to 3.7947077208293327e-22. Epoch 644/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 645/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 646/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 647/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 648/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 649/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 650/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 651/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 652/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 653/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00653: ReduceLROnPlateau reducing learning rate to 1.8973538604146664e-22. Epoch 654/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 655/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 656/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 657/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 658/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 659/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 660/1000 139/139 [==============================] - 0s 151us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 661/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 662/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 663/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00663: ReduceLROnPlateau reducing learning rate to 9.486769302073332e-23. Epoch 664/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 665/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 666/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 667/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 668/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 669/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 670/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 671/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 672/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 673/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00673: ReduceLROnPlateau reducing learning rate to 4.743384651036666e-23. Epoch 674/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 675/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 676/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 677/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 678/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 679/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 680/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 681/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 682/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 683/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00683: ReduceLROnPlateau reducing learning rate to 2.371692325518333e-23. Epoch 684/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 685/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 686/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 687/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 688/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 689/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 690/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 691/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 692/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 693/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00693: ReduceLROnPlateau reducing learning rate to 1.1858461627591665e-23. Epoch 694/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 695/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 696/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 697/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 698/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 699/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 700/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 701/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 702/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 703/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00703: ReduceLROnPlateau reducing learning rate to 5.9292308137958324e-24. Epoch 704/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 705/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 706/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 707/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 708/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 709/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 710/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 711/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 712/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 713/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00713: ReduceLROnPlateau reducing learning rate to 2.9646154068979162e-24. Epoch 714/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 715/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 716/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 717/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 718/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 719/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 720/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 721/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 722/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 723/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00723: ReduceLROnPlateau reducing learning rate to 1.4823077034489581e-24. Epoch 724/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 725/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 726/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 727/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 728/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 729/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 730/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 731/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 732/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 733/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00733: ReduceLROnPlateau reducing learning rate to 7.4115385172447905e-25. Epoch 734/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 735/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 736/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 737/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 738/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 739/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 740/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 741/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 742/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 743/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00743: ReduceLROnPlateau reducing learning rate to 3.7057692586223952e-25. Epoch 744/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 745/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 746/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 747/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 748/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 749/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 750/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 751/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 752/1000 139/139 [==============================] - 0s 165us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 753/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00753: ReduceLROnPlateau reducing learning rate to 1.8528846293111976e-25. Epoch 754/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 755/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 756/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 757/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 758/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 759/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 760/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 761/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 762/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 763/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00763: ReduceLROnPlateau reducing learning rate to 9.264423146555988e-26. Epoch 764/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 765/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 766/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 767/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 768/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 769/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 770/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 771/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 772/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 773/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00773: ReduceLROnPlateau reducing learning rate to 4.632211573277994e-26. Epoch 774/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 775/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 776/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 777/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 778/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 779/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 780/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 781/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 782/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 783/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00783: ReduceLROnPlateau reducing learning rate to 2.316105786638997e-26. Epoch 784/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 785/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 786/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 787/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 788/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 789/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 790/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 791/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 792/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 793/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00793: ReduceLROnPlateau reducing learning rate to 1.1580528933194985e-26. Epoch 794/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 795/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 796/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 797/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 798/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 799/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 800/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 801/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 802/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 803/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00803: ReduceLROnPlateau reducing learning rate to 5.7902644665974926e-27. Epoch 804/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 805/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 806/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 807/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 808/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 809/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 810/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 811/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 812/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 813/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00813: ReduceLROnPlateau reducing learning rate to 2.8951322332987463e-27. Epoch 814/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 815/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 816/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 817/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 818/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 819/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 820/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 821/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 822/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 823/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00823: ReduceLROnPlateau reducing learning rate to 1.4475661166493731e-27. Epoch 824/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 825/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 826/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 827/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 828/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 829/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 830/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 831/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 832/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 833/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00833: ReduceLROnPlateau reducing learning rate to 7.237830583246866e-28. Epoch 834/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 835/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 836/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 837/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 838/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 839/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 840/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 841/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 842/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 843/1000 139/139 [==============================] - 0s 79us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00843: ReduceLROnPlateau reducing learning rate to 3.618915291623433e-28. Epoch 844/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 845/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 846/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 847/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 848/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 849/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 850/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 851/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 852/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 853/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00853: ReduceLROnPlateau reducing learning rate to 1.8094576458117164e-28. Epoch 854/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 855/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 856/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 857/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 858/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 859/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 860/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 861/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 862/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 863/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00863: ReduceLROnPlateau reducing learning rate to 9.047288229058582e-29. Epoch 864/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 865/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 866/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 867/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 868/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 869/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 870/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 871/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 872/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 873/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00873: ReduceLROnPlateau reducing learning rate to 4.523644114529291e-29. Epoch 874/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 875/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 876/1000 139/139 [==============================] - 0s 165us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 877/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 878/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 879/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 880/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 881/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 882/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 883/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00883: ReduceLROnPlateau reducing learning rate to 2.2618220572646455e-29. Epoch 884/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 885/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 886/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 887/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 888/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 889/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 890/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 891/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 892/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 893/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00893: ReduceLROnPlateau reducing learning rate to 1.1309110286323228e-29. Epoch 894/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 895/1000 139/139 [==============================] - ETA: 0s - loss: 0.5519 - accuracy: 0.65 - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 896/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 897/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 898/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 899/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 900/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 901/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 902/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 903/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00903: ReduceLROnPlateau reducing learning rate to 5.654555143161614e-30. Epoch 904/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 905/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 906/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 907/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 908/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 909/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 910/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 911/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 912/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 913/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00913: ReduceLROnPlateau reducing learning rate to 2.827277571580807e-30. Epoch 914/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 915/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 916/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 917/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 918/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 919/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 920/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 921/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 922/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 923/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00923: ReduceLROnPlateau reducing learning rate to 1.4136387857904035e-30. Epoch 924/1000 139/139 [==============================] - 0s 151us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 925/1000 139/139 [==============================] - 0s 151us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 926/1000 139/139 [==============================] - 0s 144us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 927/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 928/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 929/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 930/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 931/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 932/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 933/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00933: ReduceLROnPlateau reducing learning rate to 7.068193928952017e-31. Epoch 934/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 935/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 936/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 937/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 938/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 939/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 940/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 941/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 942/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 943/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00943: ReduceLROnPlateau reducing learning rate to 3.5340969644760086e-31. Epoch 944/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 945/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 946/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 947/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 948/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 949/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 950/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 951/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 952/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 953/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00953: ReduceLROnPlateau reducing learning rate to 1.7670484822380043e-31. Epoch 954/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 955/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 956/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 957/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 958/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 959/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 960/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 961/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 962/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 963/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00963: ReduceLROnPlateau reducing learning rate to 8.835242411190022e-32. Epoch 964/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 965/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 966/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 967/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 968/1000 139/139 [==============================] - 0s 129us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 969/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 970/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 971/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 972/1000 139/139 [==============================] - 0s 137us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 973/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00973: ReduceLROnPlateau reducing learning rate to 4.417621205595011e-32. Epoch 974/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 975/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 976/1000 139/139 [==============================] - 0s 86us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 977/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 978/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 979/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 980/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 981/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 982/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 983/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00983: ReduceLROnPlateau reducing learning rate to 2.2088106027975054e-32. Epoch 984/1000 139/139 [==============================] - 0s 165us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 985/1000 139/139 [==============================] - 0s 151us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 986/1000 139/139 [==============================] - 0s 180us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 987/1000 139/139 [==============================] - 0s 151us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 988/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 989/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 990/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 991/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 992/1000 139/139 [==============================] - 0s 115us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 993/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 00993: ReduceLROnPlateau reducing learning rate to 1.1044053013987527e-32. Epoch 994/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 995/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 996/1000 139/139 [==============================] - 0s 108us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 997/1000 139/139 [==============================] - 0s 122us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 998/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 999/1000 139/139 [==============================] - 0s 101us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383 Epoch 1000/1000 139/139 [==============================] - 0s 93us/step - loss: 0.5600 - accuracy: 0.6906 - val_loss: 0.6272 - val_accuracy: 0.6383
In [366]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 1000)
In [367]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
47/47 [==============================] - 0s 64us/step test loss: 0.6271742037002076, test accuracy: 0.6382978558540344
In [368]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6725490196078431
In [369]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.0
KMeans¶
In [302]:
X
Out[302]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.347152 | 0.215026 | 0.572244 | 1.090273 | 0.291013 | 0.262601 |
| 1 | 0.820325 | -1.142202 | -0.584840 | -1.206404 | 0.498688 | -0.437268 |
| 2 | -0.368979 | -1.101005 | 1.316826 | 1.007755 | -0.300511 | -1.688750 |
| 3 | 0.137511 | -1.462891 | 1.217279 | 0.145184 | -0.233709 | -0.483200 |
| 4 | 0.067094 | -1.364744 | 1.323658 | -0.374292 | -0.763515 | -1.012955 |
| 5 | -0.308205 | 1.554912 | 0.177715 | -0.789365 | 0.203522 | -0.138956 |
| 6 | -0.229900 | 1.130218 | -0.432863 | -0.017215 | -0.028738 | 0.374753 |
| 7 | 0.128919 | -2.154466 | -1.473142 | -0.706391 | 0.582734 | -1.413063 |
| 8 | -0.428809 | 1.078779 | 0.122401 | 0.073561 | 1.095684 | 0.487808 |
| 9 | 0.508739 | -1.275256 | 0.146667 | 0.082460 | 0.735466 | -0.812000 |
| 10 | 0.236456 | -1.307369 | -0.087549 | -1.929049 | -0.896450 | -0.085837 |
| 11 | -0.332097 | -2.136115 | -0.379978 | 0.106336 | 0.131067 | -0.494191 |
| 12 | 0.878776 | -0.818718 | -0.318688 | -0.206650 | -0.135265 | -1.505040 |
| 13 | -0.503536 | -1.372498 | -0.663743 | -0.059237 | 0.168824 | 0.591813 |
| 14 | -0.225399 | -2.333299 | -0.977473 | -0.503168 | 0.734560 | -0.229386 |
| 15 | 1.637443 | 0.565792 | 1.742275 | -0.855040 | 0.086757 | -1.781283 |
| 16 | 1.593976 | -0.388711 | -1.659037 | 0.097761 | 0.429256 | 2.040221 |
| 17 | 1.453127 | -0.044197 | 0.718333 | 0.786724 | -0.814130 | -0.463517 |
| 18 | 0.574197 | -0.311820 | -1.315952 | 0.601889 | -0.114239 | 1.343490 |
| 19 | 1.285525 | 0.753218 | -0.678607 | 1.972188 | 0.383330 | 1.855540 |
| 20 | -0.420448 | -0.248495 | 0.126628 | -0.407281 | 0.328979 | 0.844612 |
| 21 | 0.782508 | -0.432480 | -1.645501 | -0.097137 | -0.510175 | 0.780762 |
| 22 | 1.220720 | 0.555439 | 1.643752 | 1.016671 | 0.036438 | -0.204417 |
| 23 | 1.276232 | -0.970408 | -2.218229 | -1.985819 | 0.181550 | 0.875360 |
| 24 | 0.330249 | 0.500340 | 0.945837 | 0.170144 | -1.951811 | 0.728661 |
| 25 | -0.457742 | 1.246710 | 1.511768 | -0.330001 | -0.839662 | 0.686756 |
| 26 | -0.128012 | 0.619401 | 1.421912 | -0.281963 | -2.498478 | 0.602396 |
| 27 | 1.567570 | -0.230001 | 1.237739 | 0.280013 | 0.089273 | -0.372351 |
| 28 | -0.343287 | -1.213337 | 1.470523 | -0.507041 | -1.296801 | -0.318921 |
| 29 | -1.776654 | -1.197082 | 0.552844 | -0.390964 | -0.822647 | -0.655686 |
| ... | ... | ... | ... | ... | ... | ... |
| 156 | 1.136603 | 1.061921 | -1.369527 | 0.349165 | -0.306267 | 1.859464 |
| 157 | -0.957141 | 0.412565 | -0.099705 | 0.507263 | 0.380704 | -1.450660 |
| 158 | 0.627291 | -0.982430 | 0.247369 | -0.335773 | -0.880954 | 1.943513 |
| 159 | 0.605582 | -0.713493 | -0.033525 | 0.066891 | -0.330645 | 0.762021 |
| 160 | 0.435082 | -1.765650 | -0.206944 | -2.176473 | -1.106134 | 1.293098 |
| 161 | -0.869679 | -0.284973 | -0.185255 | 1.340517 | -1.710248 | -1.319612 |
| 162 | 1.695010 | 0.478353 | -2.356288 | 0.126103 | -0.667751 | -0.738760 |
| 163 | 1.201750 | -0.589959 | -0.583958 | 1.265372 | 2.093314 | 0.102967 |
| 164 | 0.602223 | 1.408737 | 0.093544 | -1.913840 | 0.673872 | -1.282054 |
| 165 | 1.273699 | 0.835188 | 0.238069 | -0.918903 | 0.953651 | 0.759154 |
| 166 | 0.910091 | 0.783406 | -0.282440 | -0.757994 | -0.153400 | 0.541408 |
| 167 | 0.784319 | -0.628463 | 0.531487 | -0.758651 | -0.436559 | 0.237206 |
| 168 | 0.597174 | -0.260556 | 0.776122 | 0.565709 | -0.698971 | 0.850620 |
| 169 | 0.512245 | -0.906993 | 0.867897 | 0.635716 | -0.539661 | 0.041101 |
| 170 | 0.766694 | 0.045665 | 1.652744 | -0.681039 | -2.002121 | -0.208060 |
| 171 | 0.729920 | -0.012315 | 1.424850 | -1.133758 | -1.844432 | -0.865224 |
| 172 | -0.250595 | 1.333637 | 0.735398 | -2.185661 | -0.923218 | -0.293878 |
| 173 | 0.239742 | 1.083681 | -0.966488 | -0.543890 | -0.015042 | 0.298120 |
| 174 | 0.318041 | 0.919114 | -0.293494 | -1.777415 | -0.452843 | 0.237000 |
| 175 | -0.249524 | -1.310616 | -1.629695 | 0.599616 | 1.389241 | 0.190145 |
| 176 | -0.239498 | -0.884659 | -1.273002 | 0.946824 | 0.600415 | -0.458834 |
| 177 | -0.247995 | -0.379289 | 0.038455 | -0.181334 | 0.555713 | -0.343901 |
| 178 | -1.538412 | 0.875654 | -1.387902 | -0.180395 | 0.008275 | -0.438000 |
| 179 | -0.858788 | -0.062931 | -0.178983 | -0.593047 | 1.845652 | -1.559059 |
| 180 | 0.044348 | 0.420883 | -0.678748 | 1.238163 | 1.329929 | -0.157668 |
| 181 | 1.376463 | -1.287646 | 0.773584 | 1.157342 | -0.286084 | -1.054513 |
| 182 | 0.948949 | -1.179621 | 1.457273 | 1.080025 | 1.950553 | 0.140886 |
| 183 | 1.235651 | -0.462711 | 0.631757 | -1.169777 | -0.693590 | 0.281712 |
| 184 | 0.668597 | 1.124688 | -1.524719 | 0.905841 | -1.384876 | 0.539531 |
| 185 | -0.966564 | 0.725873 | 0.168576 | -0.106983 | -0.071778 | -0.257675 |
186 rows × 6 columns
In [303]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[303]:
[1116.0000000000002, 927.5012922756229, 805.362167602635, 709.3398851775523, 644.8353925193064, 590.0040583169643, 547.564638663471, 517.2051050812995, 492.673077864197, 467.91458811999206, 449.82839909018185, 426.939832241735, 408.8927579760986, 392.386338225884]
In [304]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[304]:
[<matplotlib.lines.Line2D at 0x1e8302657f0>]
K=4
In [305]:
kmeans_tc = KMeans(n_clusters=4, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[305]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [306]:
kmeans_tc.labels_
Out[306]:
array([1, 2, 2, 2, 2, 3, 1, 2, 3, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1,
2, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 1, 1, 3, 0, 0, 2, 2, 2, 1, 1,
1, 0, 3, 3, 3, 0, 3, 3, 1, 1, 1, 0, 3, 0, 1, 1, 1, 1, 2, 2, 1, 0,
2, 0, 0, 3, 3, 0, 0, 1, 1, 0, 3, 3, 1, 2, 2, 2, 2, 2, 1, 2, 3, 3,
0, 0, 0, 2, 1, 1, 3, 3, 3, 0, 2, 0, 3, 3, 3, 1, 2, 0, 3, 0, 1, 3,
2, 0, 0, 3, 0, 0, 0, 0, 2, 0, 2, 2, 3, 2, 2, 1, 2, 3, 2, 1, 2, 1,
1, 1, 3, 3, 1, 3, 1, 0, 3, 1, 3, 2, 3, 3, 3, 3, 0, 0, 0, 3, 0, 3,
1, 2, 1, 3, 1, 1, 2, 0, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3,
3, 3, 3, 3, 3, 2, 2, 2, 1, 3])
In [307]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[307]:
array([1, 2, 2, 2, 2, 3, 1, 2, 3, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1,
2, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 1, 1, 3, 0, 0, 2, 2, 2, 1, 1,
1, 0, 3, 3, 3, 0, 3, 3, 1, 1, 1, 0, 3, 0, 1, 1, 1, 1, 2, 2, 1, 0,
2, 0, 0, 3, 3, 0, 0, 1, 1, 0, 3, 3, 1, 2, 2, 2, 2, 2, 1, 2, 3, 3,
0, 0, 0, 2, 1, 1, 3, 3, 3, 0, 2, 0, 3, 3, 3, 1, 2, 0, 3, 0, 1, 3,
2, 0, 0, 3, 0, 0, 0, 0, 2, 0, 2, 2, 3, 2, 2, 1, 2, 3, 2, 1, 2, 1,
1, 1, 3, 3, 1, 3, 1, 0, 3, 1, 3, 2, 3, 3, 3, 3, 0, 0, 0, 3, 0, 3,
1, 2, 1, 3, 1, 1, 2, 0, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3,
3, 3, 3, 3, 3, 2, 2, 2, 1, 3])
In [308]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [309]:
X
Out[309]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.347152 | 0.215026 | 0.572244 | 1.090273 | 0.291013 | 0.262601 | 1 | 0 |
| 1 | 0.820325 | -1.142202 | -0.584840 | -1.206404 | 0.498688 | -0.437268 | 2 | 0 |
| 2 | -0.368979 | -1.101005 | 1.316826 | 1.007755 | -0.300511 | -1.688750 | 2 | 0 |
| 3 | 0.137511 | -1.462891 | 1.217279 | 0.145184 | -0.233709 | -0.483200 | 2 | 0 |
| 4 | 0.067094 | -1.364744 | 1.323658 | -0.374292 | -0.763515 | -1.012955 | 2 | 0 |
| 5 | -0.308205 | 1.554912 | 0.177715 | -0.789365 | 0.203522 | -0.138956 | 3 | 0 |
| 6 | -0.229900 | 1.130218 | -0.432863 | -0.017215 | -0.028738 | 0.374753 | 1 | 0 |
| 7 | 0.128919 | -2.154466 | -1.473142 | -0.706391 | 0.582734 | -1.413063 | 2 | 0 |
| 8 | -0.428809 | 1.078779 | 0.122401 | 0.073561 | 1.095684 | 0.487808 | 3 | 0 |
| 9 | 0.508739 | -1.275256 | 0.146667 | 0.082460 | 0.735466 | -0.812000 | 2 | 0 |
| 10 | 0.236456 | -1.307369 | -0.087549 | -1.929049 | -0.896450 | -0.085837 | 2 | 0 |
| 11 | -0.332097 | -2.136115 | -0.379978 | 0.106336 | 0.131067 | -0.494191 | 2 | 0 |
| 12 | 0.878776 | -0.818718 | -0.318688 | -0.206650 | -0.135265 | -1.505040 | 2 | 0 |
| 13 | -0.503536 | -1.372498 | -0.663743 | -0.059237 | 0.168824 | 0.591813 | 1 | 0 |
| 14 | -0.225399 | -2.333299 | -0.977473 | -0.503168 | 0.734560 | -0.229386 | 2 | 0 |
| 15 | 1.637443 | 0.565792 | 1.742275 | -0.855040 | 0.086757 | -1.781283 | 2 | 0 |
| 16 | 1.593976 | -0.388711 | -1.659037 | 0.097761 | 0.429256 | 2.040221 | 1 | 0 |
| 17 | 1.453127 | -0.044197 | 0.718333 | 0.786724 | -0.814130 | -0.463517 | 2 | 0 |
| 18 | 0.574197 | -0.311820 | -1.315952 | 0.601889 | -0.114239 | 1.343490 | 1 | 0 |
| 19 | 1.285525 | 0.753218 | -0.678607 | 1.972188 | 0.383330 | 1.855540 | 1 | 0 |
| 20 | -0.420448 | -0.248495 | 0.126628 | -0.407281 | 0.328979 | 0.844612 | 1 | 0 |
| 21 | 0.782508 | -0.432480 | -1.645501 | -0.097137 | -0.510175 | 0.780762 | 1 | 0 |
| 22 | 1.220720 | 0.555439 | 1.643752 | 1.016671 | 0.036438 | -0.204417 | 2 | 0 |
| 23 | 1.276232 | -0.970408 | -2.218229 | -1.985819 | 0.181550 | 0.875360 | 1 | 0 |
| 24 | 0.330249 | 0.500340 | 0.945837 | 0.170144 | -1.951811 | 0.728661 | 0 | 0 |
| 25 | -0.457742 | 1.246710 | 1.511768 | -0.330001 | -0.839662 | 0.686756 | 0 | 0 |
| 26 | -0.128012 | 0.619401 | 1.421912 | -0.281963 | -2.498478 | 0.602396 | 0 | 0 |
| 27 | 1.567570 | -0.230001 | 1.237739 | 0.280013 | 0.089273 | -0.372351 | 2 | 0 |
| 28 | -0.343287 | -1.213337 | 1.470523 | -0.507041 | -1.296801 | -0.318921 | 2 | 0 |
| 29 | -1.776654 | -1.197082 | 0.552844 | -0.390964 | -0.822647 | -0.655686 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 156 | 1.136603 | 1.061921 | -1.369527 | 0.349165 | -0.306267 | 1.859464 | 1 | 1 |
| 157 | -0.957141 | 0.412565 | -0.099705 | 0.507263 | 0.380704 | -1.450660 | 3 | 1 |
| 158 | 0.627291 | -0.982430 | 0.247369 | -0.335773 | -0.880954 | 1.943513 | 1 | 1 |
| 159 | 0.605582 | -0.713493 | -0.033525 | 0.066891 | -0.330645 | 0.762021 | 1 | 1 |
| 160 | 0.435082 | -1.765650 | -0.206944 | -2.176473 | -1.106134 | 1.293098 | 2 | 1 |
| 161 | -0.869679 | -0.284973 | -0.185255 | 1.340517 | -1.710248 | -1.319612 | 0 | 1 |
| 162 | 1.695010 | 0.478353 | -2.356288 | 0.126103 | -0.667751 | -0.738760 | 1 | 1 |
| 163 | 1.201750 | -0.589959 | -0.583958 | 1.265372 | 2.093314 | 0.102967 | 1 | 1 |
| 164 | 0.602223 | 1.408737 | 0.093544 | -1.913840 | 0.673872 | -1.282054 | 3 | 1 |
| 165 | 1.273699 | 0.835188 | 0.238069 | -0.918903 | 0.953651 | 0.759154 | 1 | 1 |
| 166 | 0.910091 | 0.783406 | -0.282440 | -0.757994 | -0.153400 | 0.541408 | 1 | 1 |
| 167 | 0.784319 | -0.628463 | 0.531487 | -0.758651 | -0.436559 | 0.237206 | 2 | 1 |
| 168 | 0.597174 | -0.260556 | 0.776122 | 0.565709 | -0.698971 | 0.850620 | 2 | 1 |
| 169 | 0.512245 | -0.906993 | 0.867897 | 0.635716 | -0.539661 | 0.041101 | 2 | 1 |
| 170 | 0.766694 | 0.045665 | 1.652744 | -0.681039 | -2.002121 | -0.208060 | 2 | 1 |
| 171 | 0.729920 | -0.012315 | 1.424850 | -1.133758 | -1.844432 | -0.865224 | 2 | 1 |
| 172 | -0.250595 | 1.333637 | 0.735398 | -2.185661 | -0.923218 | -0.293878 | 3 | 1 |
| 173 | 0.239742 | 1.083681 | -0.966488 | -0.543890 | -0.015042 | 0.298120 | 1 | 1 |
| 174 | 0.318041 | 0.919114 | -0.293494 | -1.777415 | -0.452843 | 0.237000 | 1 | 1 |
| 175 | -0.249524 | -1.310616 | -1.629695 | 0.599616 | 1.389241 | 0.190145 | 3 | 1 |
| 176 | -0.239498 | -0.884659 | -1.273002 | 0.946824 | 0.600415 | -0.458834 | 3 | 1 |
| 177 | -0.247995 | -0.379289 | 0.038455 | -0.181334 | 0.555713 | -0.343901 | 3 | 1 |
| 178 | -1.538412 | 0.875654 | -1.387902 | -0.180395 | 0.008275 | -0.438000 | 3 | 1 |
| 179 | -0.858788 | -0.062931 | -0.178983 | -0.593047 | 1.845652 | -1.559059 | 3 | 1 |
| 180 | 0.044348 | 0.420883 | -0.678748 | 1.238163 | 1.329929 | -0.157668 | 3 | 1 |
| 181 | 1.376463 | -1.287646 | 0.773584 | 1.157342 | -0.286084 | -1.054513 | 2 | 1 |
| 182 | 0.948949 | -1.179621 | 1.457273 | 1.080025 | 1.950553 | 0.140886 | 2 | 1 |
| 183 | 1.235651 | -0.462711 | 0.631757 | -1.169777 | -0.693590 | 0.281712 | 2 | 1 |
| 184 | 0.668597 | 1.124688 | -1.524719 | 0.905841 | -1.384876 | 0.539531 | 1 | 1 |
| 185 | -0.966564 | 0.725873 | 0.168576 | -0.106983 | -0.071778 | -0.257675 | 3 | 1 |
186 rows × 8 columns
In [310]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[310]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e8302a0dd8>
In [370]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [371]:
X = df_n_ps_std_tc[4]
In [372]:
y = df_n_ps[4]['chosen']
In [373]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [374]:
X_train.shape
Out[374]:
(164, 6)
In [315]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [316]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [317]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [318]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.006, 'max_iter': 400}, que permiten obtener un Accuracy de 69.51% y un Kappa del 38.98
Tiempo total: 23.88 minutos
In [375]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30,20,10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.006
epochs = 400
In [376]:
input_tensor = Input(shape = (n0,))
In [377]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [378]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [379]:
model.summary()
Model: "model_19" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_19 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_59 (Dense) (None, 30) 210 _________________________________________________________________ dense_60 (Dense) (None, 20) 620 _________________________________________________________________ dense_61 (Dense) (None, 10) 210 _________________________________________________________________ dense_62 (Dense) (None, 1) 11 ================================================================= Total params: 1,051 Trainable params: 1,051 Non-trainable params: 0 _________________________________________________________________
In [380]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/400 164/164 [==============================] - 0s 1ms/step - loss: 0.7088 - accuracy: 0.5183 - val_loss: 0.7087 - val_accuracy: 0.5455 Epoch 2/400 164/164 [==============================] - 0s 67us/step - loss: 0.6622 - accuracy: 0.6037 - val_loss: 0.6899 - val_accuracy: 0.5273 Epoch 3/400 164/164 [==============================] - 0s 79us/step - loss: 0.6587 - accuracy: 0.5793 - val_loss: 0.6864 - val_accuracy: 0.5455 Epoch 4/400 164/164 [==============================] - 0s 73us/step - loss: 0.6552 - accuracy: 0.5671 - val_loss: 0.6980 - val_accuracy: 0.4909 Epoch 5/400 164/164 [==============================] - 0s 67us/step - loss: 0.6541 - accuracy: 0.5976 - val_loss: 0.7080 - val_accuracy: 0.5273 Epoch 6/400 164/164 [==============================] - 0s 67us/step - loss: 0.6436 - accuracy: 0.6280 - val_loss: 0.7127 - val_accuracy: 0.5091 Epoch 7/400 164/164 [==============================] - 0s 73us/step - loss: 0.6387 - accuracy: 0.6159 - val_loss: 0.7140 - val_accuracy: 0.5818 Epoch 8/400 164/164 [==============================] - 0s 67us/step - loss: 0.6370 - accuracy: 0.6402 - val_loss: 0.7113 - val_accuracy: 0.5273 Epoch 9/400 164/164 [==============================] - 0s 73us/step - loss: 0.6318 - accuracy: 0.6463 - val_loss: 0.6976 - val_accuracy: 0.4727 Epoch 10/400 164/164 [==============================] - 0s 73us/step - loss: 0.6245 - accuracy: 0.6402 - val_loss: 0.6985 - val_accuracy: 0.4727 Epoch 11/400 164/164 [==============================] - 0s 67us/step - loss: 0.6202 - accuracy: 0.6402 - val_loss: 0.7012 - val_accuracy: 0.5455 Epoch 12/400 164/164 [==============================] - 0s 73us/step - loss: 0.6181 - accuracy: 0.6402 - val_loss: 0.7132 - val_accuracy: 0.5455 Epoch 13/400 164/164 [==============================] - 0s 73us/step - loss: 0.6138 - accuracy: 0.6220 - val_loss: 0.7257 - val_accuracy: 0.5455 Epoch 14/400 164/164 [==============================] - 0s 79us/step - loss: 0.6097 - accuracy: 0.6341 - val_loss: 0.7276 - val_accuracy: 0.5455 Epoch 15/400 164/164 [==============================] - 0s 79us/step - loss: 0.6004 - accuracy: 0.6463 - val_loss: 0.7047 - val_accuracy: 0.5455 Epoch 16/400 164/164 [==============================] - 0s 67us/step - loss: 0.5904 - accuracy: 0.6585 - val_loss: 0.6892 - val_accuracy: 0.5455 Epoch 17/400 164/164 [==============================] - 0s 79us/step - loss: 0.5855 - accuracy: 0.7012 - val_loss: 0.6885 - val_accuracy: 0.5273 Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.003000000026077032. Epoch 18/400 164/164 [==============================] - 0s 104us/step - loss: 0.5810 - accuracy: 0.7073 - val_loss: 0.6960 - val_accuracy: 0.5455 Epoch 19/400 164/164 [==============================] - ETA: 0s - loss: 0.5621 - accuracy: 0.68 - 0s 91us/step - loss: 0.5759 - accuracy: 0.6951 - val_loss: 0.6962 - val_accuracy: 0.5636 Epoch 20/400 164/164 [==============================] - 0s 91us/step - loss: 0.5696 - accuracy: 0.7134 - val_loss: 0.6948 - val_accuracy: 0.5455 Epoch 21/400 164/164 [==============================] - 0s 73us/step - loss: 0.5661 - accuracy: 0.7073 - val_loss: 0.6971 - val_accuracy: 0.5455 Epoch 22/400 164/164 [==============================] - 0s 73us/step - loss: 0.5649 - accuracy: 0.6707 - val_loss: 0.7056 - val_accuracy: 0.5273 Epoch 23/400 164/164 [==============================] - 0s 67us/step - loss: 0.5603 - accuracy: 0.6768 - val_loss: 0.6949 - val_accuracy: 0.4909 Epoch 24/400 164/164 [==============================] - 0s 73us/step - loss: 0.5566 - accuracy: 0.7134 - val_loss: 0.6793 - val_accuracy: 0.5455 Epoch 25/400 164/164 [==============================] - 0s 85us/step - loss: 0.5509 - accuracy: 0.7317 - val_loss: 0.6861 - val_accuracy: 0.5455 Epoch 26/400 164/164 [==============================] - 0s 97us/step - loss: 0.5448 - accuracy: 0.7073 - val_loss: 0.7017 - val_accuracy: 0.5455 Epoch 27/400 164/164 [==============================] - 0s 73us/step - loss: 0.5386 - accuracy: 0.7378 - val_loss: 0.7016 - val_accuracy: 0.5818 Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 28/400 164/164 [==============================] - 0s 79us/step - loss: 0.5355 - accuracy: 0.7378 - val_loss: 0.7024 - val_accuracy: 0.5818 Epoch 29/400 164/164 [==============================] - 0s 79us/step - loss: 0.5344 - accuracy: 0.7439 - val_loss: 0.7006 - val_accuracy: 0.5818 Epoch 30/400 164/164 [==============================] - 0s 73us/step - loss: 0.5321 - accuracy: 0.7317 - val_loss: 0.6973 - val_accuracy: 0.5818 Epoch 31/400 164/164 [==============================] - 0s 73us/step - loss: 0.5294 - accuracy: 0.7500 - val_loss: 0.6940 - val_accuracy: 0.6000 Epoch 32/400 164/164 [==============================] - 0s 73us/step - loss: 0.5256 - accuracy: 0.7439 - val_loss: 0.6926 - val_accuracy: 0.5636 Epoch 33/400 164/164 [==============================] - 0s 73us/step - loss: 0.5236 - accuracy: 0.7622 - val_loss: 0.6955 - val_accuracy: 0.5636 Epoch 34/400 164/164 [==============================] - 0s 79us/step - loss: 0.5239 - accuracy: 0.7561 - val_loss: 0.6986 - val_accuracy: 0.5455 Epoch 35/400 164/164 [==============================] - 0s 73us/step - loss: 0.5229 - accuracy: 0.7683 - val_loss: 0.6932 - val_accuracy: 0.5091 Epoch 36/400 164/164 [==============================] - 0s 73us/step - loss: 0.5181 - accuracy: 0.7805 - val_loss: 0.6923 - val_accuracy: 0.5636 Epoch 37/400 164/164 [==============================] - 0s 73us/step - loss: 0.5138 - accuracy: 0.7805 - val_loss: 0.6969 - val_accuracy: 0.5818 Epoch 38/400 164/164 [==============================] - 0s 73us/step - loss: 0.5126 - accuracy: 0.7866 - val_loss: 0.6948 - val_accuracy: 0.5455 Epoch 39/400 164/164 [==============================] - 0s 73us/step - loss: 0.5097 - accuracy: 0.7805 - val_loss: 0.6908 - val_accuracy: 0.5636 Epoch 40/400 164/164 [==============================] - 0s 73us/step - loss: 0.5074 - accuracy: 0.7805 - val_loss: 0.6882 - val_accuracy: 0.5818 Epoch 41/400 164/164 [==============================] - 0s 73us/step - loss: 0.5051 - accuracy: 0.7805 - val_loss: 0.6906 - val_accuracy: 0.5818 Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 42/400 164/164 [==============================] - 0s 73us/step - loss: 0.5034 - accuracy: 0.7744 - val_loss: 0.6922 - val_accuracy: 0.5818 Epoch 43/400 164/164 [==============================] - 0s 79us/step - loss: 0.5023 - accuracy: 0.7744 - val_loss: 0.6940 - val_accuracy: 0.5818 Epoch 44/400 164/164 [==============================] - 0s 73us/step - loss: 0.5019 - accuracy: 0.7744 - val_loss: 0.6961 - val_accuracy: 0.5636 Epoch 45/400 164/164 [==============================] - 0s 73us/step - loss: 0.5010 - accuracy: 0.7622 - val_loss: 0.6992 - val_accuracy: 0.5636 Epoch 46/400 164/164 [==============================] - 0s 73us/step - loss: 0.4997 - accuracy: 0.7683 - val_loss: 0.6997 - val_accuracy: 0.5636 Epoch 47/400 164/164 [==============================] - 0s 79us/step - loss: 0.4982 - accuracy: 0.7683 - val_loss: 0.6998 - val_accuracy: 0.5636 Epoch 48/400 164/164 [==============================] - 0s 110us/step - loss: 0.4969 - accuracy: 0.7683 - val_loss: 0.7021 - val_accuracy: 0.5636 Epoch 49/400 164/164 [==============================] - 0s 104us/step - loss: 0.4955 - accuracy: 0.7683 - val_loss: 0.7023 - val_accuracy: 0.5818 Epoch 50/400 164/164 [==============================] - 0s 91us/step - loss: 0.4943 - accuracy: 0.7683 - val_loss: 0.7007 - val_accuracy: 0.5818 Epoch 51/400 164/164 [==============================] - 0s 91us/step - loss: 0.4931 - accuracy: 0.7683 - val_loss: 0.6979 - val_accuracy: 0.5818 Epoch 00051: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 52/400 164/164 [==============================] - 0s 73us/step - loss: 0.4913 - accuracy: 0.7805 - val_loss: 0.6963 - val_accuracy: 0.5636 Epoch 53/400 164/164 [==============================] - 0s 79us/step - loss: 0.4910 - accuracy: 0.7805 - val_loss: 0.6947 - val_accuracy: 0.5818 Epoch 54/400 164/164 [==============================] - 0s 79us/step - loss: 0.4902 - accuracy: 0.7866 - val_loss: 0.6944 - val_accuracy: 0.5818 Epoch 55/400 164/164 [==============================] - 0s 73us/step - loss: 0.4896 - accuracy: 0.7866 - val_loss: 0.6946 - val_accuracy: 0.5636 Epoch 56/400 164/164 [==============================] - 0s 79us/step - loss: 0.4889 - accuracy: 0.7866 - val_loss: 0.6945 - val_accuracy: 0.5636 Epoch 57/400 164/164 [==============================] - 0s 85us/step - loss: 0.4881 - accuracy: 0.7927 - val_loss: 0.6952 - val_accuracy: 0.5636 Epoch 58/400 164/164 [==============================] - 0s 73us/step - loss: 0.4879 - accuracy: 0.7927 - val_loss: 0.6949 - val_accuracy: 0.5636 Epoch 59/400 164/164 [==============================] - 0s 79us/step - loss: 0.4871 - accuracy: 0.7927 - val_loss: 0.6967 - val_accuracy: 0.5636 Epoch 60/400 164/164 [==============================] - 0s 98us/step - loss: 0.4867 - accuracy: 0.7988 - val_loss: 0.6976 - val_accuracy: 0.5636 Epoch 61/400 164/164 [==============================] - 0s 85us/step - loss: 0.4860 - accuracy: 0.7988 - val_loss: 0.6975 - val_accuracy: 0.5636 Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 62/400 164/164 [==============================] - 0s 79us/step - loss: 0.4855 - accuracy: 0.7988 - val_loss: 0.6974 - val_accuracy: 0.5636 Epoch 63/400 164/164 [==============================] - 0s 104us/step - loss: 0.4852 - accuracy: 0.8049 - val_loss: 0.6974 - val_accuracy: 0.5636 Epoch 64/400 164/164 [==============================] - 0s 79us/step - loss: 0.4849 - accuracy: 0.8049 - val_loss: 0.6973 - val_accuracy: 0.5636 Epoch 65/400 164/164 [==============================] - 0s 79us/step - loss: 0.4844 - accuracy: 0.7927 - val_loss: 0.6977 - val_accuracy: 0.5636 Epoch 66/400 164/164 [==============================] - 0s 79us/step - loss: 0.4840 - accuracy: 0.7927 - val_loss: 0.6983 - val_accuracy: 0.5636 Epoch 67/400 164/164 [==============================] - 0s 67us/step - loss: 0.4837 - accuracy: 0.7927 - val_loss: 0.6988 - val_accuracy: 0.5636 Epoch 68/400 164/164 [==============================] - 0s 67us/step - loss: 0.4835 - accuracy: 0.7988 - val_loss: 0.6992 - val_accuracy: 0.5455 Epoch 69/400 164/164 [==============================] - 0s 73us/step - loss: 0.4830 - accuracy: 0.7988 - val_loss: 0.6988 - val_accuracy: 0.5455 Epoch 70/400 164/164 [==============================] - 0s 67us/step - loss: 0.4829 - accuracy: 0.7988 - val_loss: 0.6984 - val_accuracy: 0.5455 Epoch 71/400 164/164 [==============================] - 0s 67us/step - loss: 0.4828 - accuracy: 0.7927 - val_loss: 0.6973 - val_accuracy: 0.5455 Epoch 00071: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 72/400 164/164 [==============================] - 0s 73us/step - loss: 0.4824 - accuracy: 0.7988 - val_loss: 0.6970 - val_accuracy: 0.5455 Epoch 73/400 164/164 [==============================] - 0s 128us/step - loss: 0.4822 - accuracy: 0.8049 - val_loss: 0.6972 - val_accuracy: 0.5455 Epoch 74/400 164/164 [==============================] - 0s 79us/step - loss: 0.4820 - accuracy: 0.8110 - val_loss: 0.6971 - val_accuracy: 0.5455 Epoch 75/400 164/164 [==============================] - 0s 79us/step - loss: 0.4820 - accuracy: 0.8110 - val_loss: 0.6972 - val_accuracy: 0.5455 Epoch 76/400 164/164 [==============================] - 0s 73us/step - loss: 0.4818 - accuracy: 0.8110 - val_loss: 0.6969 - val_accuracy: 0.5455 Epoch 77/400 164/164 [==============================] - 0s 67us/step - loss: 0.4816 - accuracy: 0.8110 - val_loss: 0.6966 - val_accuracy: 0.5455 Epoch 78/400 164/164 [==============================] - 0s 79us/step - loss: 0.4814 - accuracy: 0.8110 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 79/400 164/164 [==============================] - 0s 79us/step - loss: 0.4812 - accuracy: 0.8110 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 80/400 164/164 [==============================] - 0s 73us/step - loss: 0.4811 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 81/400 164/164 [==============================] - 0s 73us/step - loss: 0.4810 - accuracy: 0.7988 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 00081: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 82/400 164/164 [==============================] - 0s 73us/step - loss: 0.4808 - accuracy: 0.7988 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 83/400 164/164 [==============================] - 0s 85us/step - loss: 0.4807 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 84/400 164/164 [==============================] - 0s 73us/step - loss: 0.4806 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 85/400 164/164 [==============================] - 0s 67us/step - loss: 0.4805 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 86/400 164/164 [==============================] - 0s 91us/step - loss: 0.4805 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 87/400 164/164 [==============================] - 0s 104us/step - loss: 0.4804 - accuracy: 0.8049 - val_loss: 0.6966 - val_accuracy: 0.5455 Epoch 88/400 164/164 [==============================] - 0s 91us/step - loss: 0.4804 - accuracy: 0.8049 - val_loss: 0.6967 - val_accuracy: 0.5455 Epoch 89/400 164/164 [==============================] - 0s 91us/step - loss: 0.4803 - accuracy: 0.8049 - val_loss: 0.6968 - val_accuracy: 0.5455 Epoch 90/400 164/164 [==============================] - 0s 73us/step - loss: 0.4802 - accuracy: 0.8049 - val_loss: 0.6969 - val_accuracy: 0.5455 Epoch 91/400 164/164 [==============================] - 0s 79us/step - loss: 0.4801 - accuracy: 0.8049 - val_loss: 0.6968 - val_accuracy: 0.5455 Epoch 00091: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 92/400 164/164 [==============================] - 0s 67us/step - loss: 0.4800 - accuracy: 0.8049 - val_loss: 0.6967 - val_accuracy: 0.5455 Epoch 93/400 164/164 [==============================] - 0s 67us/step - loss: 0.4800 - accuracy: 0.8049 - val_loss: 0.6967 - val_accuracy: 0.5455 Epoch 94/400 164/164 [==============================] - 0s 73us/step - loss: 0.4800 - accuracy: 0.8049 - val_loss: 0.6966 - val_accuracy: 0.5455 Epoch 95/400 164/164 [==============================] - 0s 73us/step - loss: 0.4799 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 96/400 164/164 [==============================] - 0s 73us/step - loss: 0.4799 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 97/400 164/164 [==============================] - 0s 67us/step - loss: 0.4798 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 98/400 164/164 [==============================] - 0s 73us/step - loss: 0.4798 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 99/400 164/164 [==============================] - 0s 79us/step - loss: 0.4797 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 100/400 164/164 [==============================] - 0s 104us/step - loss: 0.4797 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 101/400 164/164 [==============================] - 0s 85us/step - loss: 0.4797 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 00101: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 102/400 164/164 [==============================] - 0s 73us/step - loss: 0.4796 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 103/400 164/164 [==============================] - 0s 85us/step - loss: 0.4796 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 104/400 164/164 [==============================] - 0s 73us/step - loss: 0.4795 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 105/400 164/164 [==============================] - 0s 79us/step - loss: 0.4795 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 106/400 164/164 [==============================] - 0s 85us/step - loss: 0.4795 - accuracy: 0.8049 - val_loss: 0.6963 - val_accuracy: 0.5455 Epoch 107/400 164/164 [==============================] - 0s 79us/step - loss: 0.4795 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 108/400 164/164 [==============================] - 0s 73us/step - loss: 0.4794 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 109/400 164/164 [==============================] - 0s 73us/step - loss: 0.4794 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 110/400 164/164 [==============================] - 0s 73us/step - loss: 0.4794 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 111/400 164/164 [==============================] - 0s 73us/step - loss: 0.4794 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 00111: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 112/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 113/400 164/164 [==============================] - 0s 67us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 114/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 115/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 116/400 164/164 [==============================] - 0s 79us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 117/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 118/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 119/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 120/400 164/164 [==============================] - 0s 79us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 121/400 164/164 [==============================] - 0s 73us/step - loss: 0.4793 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 00121: ReduceLROnPlateau reducing learning rate to 2.9296875254658516e-06. Epoch 122/400 164/164 [==============================] - 0s 67us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 123/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 124/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 125/400 164/164 [==============================] - 0s 85us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 126/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 127/400 164/164 [==============================] - 0s 91us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 128/400 164/164 [==============================] - 0s 79us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 129/400 164/164 [==============================] - 0s 79us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 130/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 131/400 164/164 [==============================] - 0s 85us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00131: ReduceLROnPlateau reducing learning rate to 1.4648437627329258e-06. Epoch 132/400 164/164 [==============================] - 0s 91us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 133/400 164/164 [==============================] - 0s 79us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 134/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 135/400 164/164 [==============================] - 0s 122us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 136/400 164/164 [==============================] - 0s 85us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 137/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6965 - val_accuracy: 0.5455 Epoch 138/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 139/400 164/164 [==============================] - 0s 122us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 140/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 141/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00141: ReduceLROnPlateau reducing learning rate to 7.324218813664629e-07. Epoch 142/400 164/164 [==============================] - 0s 79us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 143/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 144/400 164/164 [==============================] - 0s 79us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 145/400 164/164 [==============================] - 0s 73us/step - loss: 0.4792 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 146/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 147/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 148/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 149/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 150/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 151/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00151: ReduceLROnPlateau reducing learning rate to 3.6621094068323146e-07. Epoch 152/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 153/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 154/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 155/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 156/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 157/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 158/400 164/164 [==============================] - 0s 122us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 159/400 164/164 [==============================] - 0s 110us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 160/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 161/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00161: ReduceLROnPlateau reducing learning rate to 1.8310547034161573e-07. Epoch 162/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 163/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 164/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 165/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 166/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 167/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 168/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 169/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 170/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 171/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00171: ReduceLROnPlateau reducing learning rate to 9.155273517080786e-08. Epoch 172/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 173/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 174/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 175/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 176/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 177/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 178/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 179/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 180/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 181/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00181: ReduceLROnPlateau reducing learning rate to 4.577636758540393e-08. Epoch 182/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 183/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 184/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 185/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 186/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 187/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 188/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 189/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 190/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 191/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00191: ReduceLROnPlateau reducing learning rate to 2.2888183792701966e-08. Epoch 192/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 193/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 194/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 195/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 196/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 197/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 198/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 199/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 200/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 201/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00201: ReduceLROnPlateau reducing learning rate to 1.1444091896350983e-08. Epoch 202/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 203/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 204/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 205/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 206/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 207/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 208/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 209/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 210/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 211/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00211: ReduceLROnPlateau reducing learning rate to 5.7220459481754915e-09. Epoch 212/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 213/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 214/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 215/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 216/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 217/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 218/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 219/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 220/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 221/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00221: ReduceLROnPlateau reducing learning rate to 2.8610229740877458e-09. Epoch 222/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 223/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 224/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 225/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 226/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 227/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 228/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 229/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 230/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 231/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00231: ReduceLROnPlateau reducing learning rate to 1.4305114870438729e-09. Epoch 232/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 233/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 234/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 235/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 236/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 237/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 238/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 239/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 240/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 241/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00241: ReduceLROnPlateau reducing learning rate to 7.152557435219364e-10. Epoch 242/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 243/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 244/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 245/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 246/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 247/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 248/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 249/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 250/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 251/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00251: ReduceLROnPlateau reducing learning rate to 3.576278717609682e-10. Epoch 252/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 253/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 254/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 255/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 256/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 257/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 258/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 259/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 260/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 261/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00261: ReduceLROnPlateau reducing learning rate to 1.788139358804841e-10. Epoch 262/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 263/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 264/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 265/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 266/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 267/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 268/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 269/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 270/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 271/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00271: ReduceLROnPlateau reducing learning rate to 8.940696794024205e-11. Epoch 272/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 273/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 274/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 275/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 276/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 277/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 278/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 279/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 280/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 281/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00281: ReduceLROnPlateau reducing learning rate to 4.470348397012103e-11. Epoch 282/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 283/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 284/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 285/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 286/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 287/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 288/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 289/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 290/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 291/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00291: ReduceLROnPlateau reducing learning rate to 2.2351741985060514e-11. Epoch 292/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 293/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 294/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 295/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 296/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 297/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 298/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 299/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 300/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 301/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00301: ReduceLROnPlateau reducing learning rate to 1.1175870992530257e-11. Epoch 302/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 303/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 304/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 305/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 306/400 164/164 [==============================] - 0s 116us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 307/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 308/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 309/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 310/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 311/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00311: ReduceLROnPlateau reducing learning rate to 5.5879354962651284e-12. Epoch 312/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 313/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 314/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 315/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 316/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 317/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 318/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 319/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 320/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 321/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00321: ReduceLROnPlateau reducing learning rate to 2.7939677481325642e-12. Epoch 322/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 323/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 324/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 325/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 326/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 327/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 328/400 164/164 [==============================] - 0s 116us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 329/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 330/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 331/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00331: ReduceLROnPlateau reducing learning rate to 1.3969838740662821e-12. Epoch 332/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 333/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 334/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 335/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 336/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 337/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 338/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 339/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 340/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 341/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00341: ReduceLROnPlateau reducing learning rate to 6.984919370331411e-13. Epoch 342/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 343/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 344/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 345/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 346/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 347/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 348/400 164/164 [==============================] - 0s 128us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 349/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 350/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 351/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00351: ReduceLROnPlateau reducing learning rate to 3.4924596851657053e-13. Epoch 352/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 353/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 354/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 355/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 356/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 357/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 358/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 359/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 360/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 361/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00361: ReduceLROnPlateau reducing learning rate to 1.7462298425828526e-13. Epoch 362/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 363/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 364/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 365/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 366/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 367/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 368/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 369/400 164/164 [==============================] - 0s 122us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 370/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 371/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00371: ReduceLROnPlateau reducing learning rate to 8.731149212914263e-14. Epoch 372/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 373/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 374/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 375/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 376/400 164/164 [==============================] - 0s 98us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 377/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 378/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 379/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 380/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 381/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00381: ReduceLROnPlateau reducing learning rate to 4.3655746064571316e-14. Epoch 382/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 383/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 384/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 385/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 386/400 164/164 [==============================] - 0s 85us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 387/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 388/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 389/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 390/400 164/164 [==============================] - 0s 104us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 391/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 00391: ReduceLROnPlateau reducing learning rate to 2.1827873032285658e-14. Epoch 392/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 393/400 164/164 [==============================] - 0s 73us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 394/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 395/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 396/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 397/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 398/400 164/164 [==============================] - 0s 91us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 399/400 164/164 [==============================] - 0s 79us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455 Epoch 400/400 164/164 [==============================] - 0s 67us/step - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.6964 - val_accuracy: 0.5455
In [381]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 400)
In [382]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 73us/step test loss: 0.696373957937414, test accuracy: 0.5454545617103577
In [383]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6058201058201058
In [384]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.09120951751487105
KMeans¶
In [329]:
X
Out[329]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | -1.035481 | 1.779354 | 1.874576 | 0.924814 | -0.129662 | 2.608421 |
| 1 | 0.965487 | -0.399971 | -1.606069 | 0.008311 | 0.834341 | 0.513694 |
| 2 | -0.141249 | -1.969933 | -0.960470 | 1.005123 | -1.117123 | -2.399517 |
| 3 | -1.590590 | -0.729741 | -0.575342 | 0.587988 | -0.885561 | -0.752828 |
| 4 | -0.391524 | -0.894181 | -0.426309 | 1.017585 | -0.391173 | -0.920259 |
| 5 | -1.256622 | -0.886861 | -0.850243 | 0.516749 | -0.491454 | -0.072867 |
| 6 | -1.579202 | 0.121365 | -0.522749 | 1.012025 | -0.547676 | -0.140430 |
| 7 | -1.760350 | -0.182429 | -0.008789 | 1.576085 | -0.878841 | 0.252104 |
| 8 | 1.115526 | 1.555384 | 0.609404 | 0.558809 | -0.514428 | -0.221726 |
| 9 | 1.467291 | 1.402697 | 0.806896 | -0.279535 | 0.939735 | 0.758333 |
| 10 | 0.972379 | 1.550575 | -0.223468 | 0.899199 | 1.412818 | 0.386724 |
| 11 | 0.294385 | 0.890870 | 0.493531 | 0.142145 | 0.212432 | 1.463886 |
| 12 | 0.795134 | 0.176458 | 1.588747 | -0.412034 | -0.982878 | -0.299581 |
| 13 | 0.694481 | 0.577820 | 0.319393 | 0.451356 | 0.219257 | 0.563199 |
| 14 | 1.169114 | 0.075245 | -0.980006 | 1.330732 | 2.094068 | 1.785970 |
| 15 | 0.962642 | 0.380225 | -1.261850 | 1.044019 | 1.339949 | 1.776870 |
| 16 | 1.352245 | 0.463507 | -0.679184 | 0.941519 | 2.196602 | 1.991369 |
| 17 | 1.784002 | -1.453636 | -1.128885 | -0.626496 | 0.399672 | -0.605861 |
| 18 | 0.929212 | -0.538274 | -1.016394 | -0.167176 | 0.557933 | 1.511009 |
| 19 | 1.199761 | -0.727252 | 0.322239 | -1.105069 | -0.125311 | -0.979170 |
| 20 | -0.485056 | 0.796900 | 0.581966 | 1.884586 | -0.705890 | -0.725300 |
| 21 | -0.547233 | 0.692440 | -0.162284 | 2.025268 | -0.631876 | -0.337240 |
| 22 | 1.446103 | -0.074850 | -0.132752 | -0.064117 | -0.209506 | -0.465551 |
| 23 | -0.312063 | 0.030270 | -1.160963 | 0.726155 | -1.511552 | -0.509175 |
| 24 | 1.175126 | -0.143713 | -0.522479 | 0.641015 | 0.500311 | 0.617748 |
| 25 | -1.044292 | -0.058933 | -1.340279 | -1.302246 | 1.751828 | -0.815403 |
| 26 | -0.849044 | 0.079838 | -0.400536 | -1.312330 | 1.498217 | -0.550869 |
| 27 | -0.730672 | -0.326196 | -0.478608 | -0.832610 | -0.556236 | -0.653280 |
| 28 | -0.380922 | -0.892886 | -0.555313 | -0.113628 | 1.211258 | -0.901155 |
| 29 | -0.368302 | -1.168844 | -0.094765 | -0.158075 | 1.016584 | -1.274561 |
| ... | ... | ... | ... | ... | ... | ... |
| 189 | -1.023243 | 0.827082 | 0.695531 | 0.482823 | -0.093190 | -0.130945 |
| 190 | 1.643548 | -0.570770 | 0.545333 | -0.137189 | 0.295910 | -0.891672 |
| 191 | 1.543182 | -0.533850 | 0.979103 | 0.227528 | 0.216491 | -0.016099 |
| 192 | 1.416929 | -1.770555 | 0.592692 | -1.546796 | -0.112419 | -0.017441 |
| 193 | -1.336444 | 0.162214 | -1.528887 | 1.340066 | 0.343647 | -0.060973 |
| 194 | -0.331197 | -0.545328 | 0.449891 | -2.242097 | 0.210220 | 1.299600 |
| 195 | -0.991382 | -0.378373 | -0.215170 | -2.818431 | 1.156878 | -0.599042 |
| 196 | 0.827092 | 0.502299 | 0.219306 | 1.474834 | 0.577530 | 0.832676 |
| 197 | 0.976291 | 0.325663 | -0.091820 | 0.723604 | 0.494609 | 0.610596 |
| 198 | 0.903378 | 0.857383 | 0.090549 | 0.948012 | 1.127442 | 0.927032 |
| 199 | -1.135922 | -0.217483 | -0.201444 | 0.204262 | -0.033230 | -0.725561 |
| 200 | -1.143077 | -0.289624 | -0.109440 | 0.093244 | 0.007101 | -0.571608 |
| 201 | -1.325584 | -0.109383 | -0.850284 | -0.442939 | 0.518129 | -0.996845 |
| 202 | 0.270878 | 1.568003 | -0.899682 | 0.187348 | -0.995623 | 0.436835 |
| 203 | -0.010376 | 1.403657 | -0.298654 | 0.126520 | -0.803249 | -0.284875 |
| 204 | -0.149606 | 0.679408 | -0.527828 | 0.145473 | 0.226461 | 0.232361 |
| 205 | -1.281900 | 0.472582 | 2.041397 | -0.186464 | 1.140780 | -0.694445 |
| 206 | -1.561361 | 0.699591 | 0.373931 | 0.512801 | 0.245563 | -1.259098 |
| 207 | -0.548022 | 0.646014 | -0.015758 | -0.364427 | 1.106060 | -0.395692 |
| 208 | -0.689835 | 0.729721 | 0.242422 | 0.167324 | -0.269920 | 0.625568 |
| 209 | -1.182263 | 0.898528 | 0.655331 | 1.146978 | -0.973699 | 0.509883 |
| 210 | -0.465862 | 0.576977 | -0.088421 | 1.290934 | 0.648005 | 0.669298 |
| 211 | -0.265321 | 1.252143 | 0.230904 | 0.383047 | -0.920749 | 0.237760 |
| 212 | 0.205358 | 1.300786 | 0.929349 | -0.432002 | -0.464366 | -0.242135 |
| 213 | -0.025600 | 0.467818 | 0.261063 | -1.437444 | -0.391460 | -0.995280 |
| 214 | -1.082557 | 1.025513 | 2.276661 | 1.056731 | 0.361540 | 1.291351 |
| 215 | -1.297371 | 1.948703 | 2.264684 | 1.377703 | 1.194669 | 1.983124 |
| 216 | -0.926424 | 0.162164 | 1.016687 | 1.945841 | -1.341651 | 0.150826 |
| 217 | -1.375041 | -0.362757 | -0.599873 | 1.478900 | -0.021584 | -0.846072 |
| 218 | -0.974264 | 0.740461 | 0.889462 | 0.014997 | 1.024334 | -0.992000 |
219 rows × 6 columns
In [330]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[330]:
[1314.0, 1103.6617898421102, 933.9046374976435, 830.0952355796812, 752.8157274494505, 696.8283563577859, 641.355058887789, 599.6834692450786, 558.0899857646746, 538.6016435622136, 502.540180641064, 477.03865333096127, 457.1745404655215, 443.08717934712786]
In [331]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[331]:
[<matplotlib.lines.Line2D at 0x1e831ea44e0>]
K=3
In [332]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[332]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [333]:
kmeans_tc.labels_
Out[333]:
array([2, 2, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2,
0, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
2, 2, 1, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 2,
0, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 2, 0,
2, 1, 1, 2, 2, 1, 2, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 1, 1, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,
2, 0, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1,
1, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2,
2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1])
In [334]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[334]:
array([2, 2, 0, 1, 0, 1, 1, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 2, 2,
0, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,
2, 2, 1, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 2,
0, 0, 0, 0, 1, 0, 2, 2, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 2, 0,
2, 1, 1, 2, 2, 1, 2, 2, 2, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 1, 1, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,
2, 0, 0, 0, 2, 0, 0, 2, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
0, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1,
1, 0, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2,
2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1])
In [335]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [336]:
X
Out[336]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | -1.035481 | 1.779354 | 1.874576 | 0.924814 | -0.129662 | 2.608421 | 2 | 0 |
| 1 | 0.965487 | -0.399971 | -1.606069 | 0.008311 | 0.834341 | 0.513694 | 2 | 0 |
| 2 | -0.141249 | -1.969933 | -0.960470 | 1.005123 | -1.117123 | -2.399517 | 0 | 0 |
| 3 | -1.590590 | -0.729741 | -0.575342 | 0.587988 | -0.885561 | -0.752828 | 1 | 0 |
| 4 | -0.391524 | -0.894181 | -0.426309 | 1.017585 | -0.391173 | -0.920259 | 0 | 0 |
| 5 | -1.256622 | -0.886861 | -0.850243 | 0.516749 | -0.491454 | -0.072867 | 1 | 0 |
| 6 | -1.579202 | 0.121365 | -0.522749 | 1.012025 | -0.547676 | -0.140430 | 1 | 0 |
| 7 | -1.760350 | -0.182429 | -0.008789 | 1.576085 | -0.878841 | 0.252104 | 2 | 0 |
| 8 | 1.115526 | 1.555384 | 0.609404 | 0.558809 | -0.514428 | -0.221726 | 2 | 0 |
| 9 | 1.467291 | 1.402697 | 0.806896 | -0.279535 | 0.939735 | 0.758333 | 2 | 0 |
| 10 | 0.972379 | 1.550575 | -0.223468 | 0.899199 | 1.412818 | 0.386724 | 2 | 0 |
| 11 | 0.294385 | 0.890870 | 0.493531 | 0.142145 | 0.212432 | 1.463886 | 2 | 0 |
| 12 | 0.795134 | 0.176458 | 1.588747 | -0.412034 | -0.982878 | -0.299581 | 0 | 0 |
| 13 | 0.694481 | 0.577820 | 0.319393 | 0.451356 | 0.219257 | 0.563199 | 2 | 0 |
| 14 | 1.169114 | 0.075245 | -0.980006 | 1.330732 | 2.094068 | 1.785970 | 2 | 0 |
| 15 | 0.962642 | 0.380225 | -1.261850 | 1.044019 | 1.339949 | 1.776870 | 2 | 0 |
| 16 | 1.352245 | 0.463507 | -0.679184 | 0.941519 | 2.196602 | 1.991369 | 2 | 0 |
| 17 | 1.784002 | -1.453636 | -1.128885 | -0.626496 | 0.399672 | -0.605861 | 0 | 0 |
| 18 | 0.929212 | -0.538274 | -1.016394 | -0.167176 | 0.557933 | 1.511009 | 2 | 0 |
| 19 | 1.199761 | -0.727252 | 0.322239 | -1.105069 | -0.125311 | -0.979170 | 0 | 0 |
| 20 | -0.485056 | 0.796900 | 0.581966 | 1.884586 | -0.705890 | -0.725300 | 2 | 0 |
| 21 | -0.547233 | 0.692440 | -0.162284 | 2.025268 | -0.631876 | -0.337240 | 2 | 0 |
| 22 | 1.446103 | -0.074850 | -0.132752 | -0.064117 | -0.209506 | -0.465551 | 0 | 0 |
| 23 | -0.312063 | 0.030270 | -1.160963 | 0.726155 | -1.511552 | -0.509175 | 2 | 0 |
| 24 | 1.175126 | -0.143713 | -0.522479 | 0.641015 | 0.500311 | 0.617748 | 2 | 0 |
| 25 | -1.044292 | -0.058933 | -1.340279 | -1.302246 | 1.751828 | -0.815403 | 1 | 0 |
| 26 | -0.849044 | 0.079838 | -0.400536 | -1.312330 | 1.498217 | -0.550869 | 1 | 0 |
| 27 | -0.730672 | -0.326196 | -0.478608 | -0.832610 | -0.556236 | -0.653280 | 1 | 0 |
| 28 | -0.380922 | -0.892886 | -0.555313 | -0.113628 | 1.211258 | -0.901155 | 1 | 0 |
| 29 | -0.368302 | -1.168844 | -0.094765 | -0.158075 | 1.016584 | -1.274561 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -1.023243 | 0.827082 | 0.695531 | 0.482823 | -0.093190 | -0.130945 | 2 | 1 |
| 190 | 1.643548 | -0.570770 | 0.545333 | -0.137189 | 0.295910 | -0.891672 | 0 | 1 |
| 191 | 1.543182 | -0.533850 | 0.979103 | 0.227528 | 0.216491 | -0.016099 | 0 | 1 |
| 192 | 1.416929 | -1.770555 | 0.592692 | -1.546796 | -0.112419 | -0.017441 | 0 | 1 |
| 193 | -1.336444 | 0.162214 | -1.528887 | 1.340066 | 0.343647 | -0.060973 | 1 | 1 |
| 194 | -0.331197 | -0.545328 | 0.449891 | -2.242097 | 0.210220 | 1.299600 | 0 | 1 |
| 195 | -0.991382 | -0.378373 | -0.215170 | -2.818431 | 1.156878 | -0.599042 | 1 | 1 |
| 196 | 0.827092 | 0.502299 | 0.219306 | 1.474834 | 0.577530 | 0.832676 | 2 | 1 |
| 197 | 0.976291 | 0.325663 | -0.091820 | 0.723604 | 0.494609 | 0.610596 | 2 | 1 |
| 198 | 0.903378 | 0.857383 | 0.090549 | 0.948012 | 1.127442 | 0.927032 | 2 | 1 |
| 199 | -1.135922 | -0.217483 | -0.201444 | 0.204262 | -0.033230 | -0.725561 | 1 | 1 |
| 200 | -1.143077 | -0.289624 | -0.109440 | 0.093244 | 0.007101 | -0.571608 | 1 | 1 |
| 201 | -1.325584 | -0.109383 | -0.850284 | -0.442939 | 0.518129 | -0.996845 | 1 | 1 |
| 202 | 0.270878 | 1.568003 | -0.899682 | 0.187348 | -0.995623 | 0.436835 | 2 | 1 |
| 203 | -0.010376 | 1.403657 | -0.298654 | 0.126520 | -0.803249 | -0.284875 | 2 | 1 |
| 204 | -0.149606 | 0.679408 | -0.527828 | 0.145473 | 0.226461 | 0.232361 | 2 | 1 |
| 205 | -1.281900 | 0.472582 | 2.041397 | -0.186464 | 1.140780 | -0.694445 | 1 | 1 |
| 206 | -1.561361 | 0.699591 | 0.373931 | 0.512801 | 0.245563 | -1.259098 | 1 | 1 |
| 207 | -0.548022 | 0.646014 | -0.015758 | -0.364427 | 1.106060 | -0.395692 | 1 | 1 |
| 208 | -0.689835 | 0.729721 | 0.242422 | 0.167324 | -0.269920 | 0.625568 | 2 | 1 |
| 209 | -1.182263 | 0.898528 | 0.655331 | 1.146978 | -0.973699 | 0.509883 | 2 | 1 |
| 210 | -0.465862 | 0.576977 | -0.088421 | 1.290934 | 0.648005 | 0.669298 | 2 | 1 |
| 211 | -0.265321 | 1.252143 | 0.230904 | 0.383047 | -0.920749 | 0.237760 | 2 | 1 |
| 212 | 0.205358 | 1.300786 | 0.929349 | -0.432002 | -0.464366 | -0.242135 | 2 | 1 |
| 213 | -0.025600 | 0.467818 | 0.261063 | -1.437444 | -0.391460 | -0.995280 | 1 | 1 |
| 214 | -1.082557 | 1.025513 | 2.276661 | 1.056731 | 0.361540 | 1.291351 | 2 | 1 |
| 215 | -1.297371 | 1.948703 | 2.264684 | 1.377703 | 1.194669 | 1.983124 | 2 | 1 |
| 216 | -0.926424 | 0.162164 | 1.016687 | 1.945841 | -1.341651 | 0.150826 | 2 | 1 |
| 217 | -1.375041 | -0.362757 | -0.599873 | 1.478900 | -0.021584 | -0.846072 | 1 | 1 |
| 218 | -0.974264 | 0.740461 | 0.889462 | 0.014997 | 1.024334 | -0.992000 | 1 | 1 |
219 rows × 8 columns
In [337]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[337]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e832243f98>
In [385]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [386]:
X = df_n_ps_std_tc[5]
In [387]:
y = df_n_ps[5]['chosen']
In [388]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [389]:
X_train.shape
Out[389]:
(168, 6)
In [343]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [344]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [345]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [346]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20, 10), 'learning_rate_init': 0.005, 'max_iter': 200}, que permiten obtener un Accuracy de 70.83% y un Kappa del 35.52
Tiempo total: 28.81 minutos
In [390]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20,10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = grid.best_params_['learning_rate_init']
epochs = grid.best_params_['max_iter']
In [391]:
input_tensor = Input(shape = (n0,))
In [392]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [393]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [394]:
model.summary()
Model: "model_20" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_20 (InputLayer) (None, 6) 0 _________________________________________________________________ dense_63 (Dense) (None, 20) 140 _________________________________________________________________ dense_64 (Dense) (None, 10) 210 _________________________________________________________________ dense_65 (Dense) (None, 1) 11 ================================================================= Total params: 361 Trainable params: 361 Non-trainable params: 0 _________________________________________________________________
In [395]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 168 samples, validate on 57 samples Epoch 1/100 168/168 [==============================] - 0s 1ms/step - loss: 0.7516 - accuracy: 0.4524 - val_loss: 0.6534 - val_accuracy: 0.6316 Epoch 2/100 168/168 [==============================] - 0s 95us/step - loss: 0.6794 - accuracy: 0.5536 - val_loss: 0.6620 - val_accuracy: 0.5789 Epoch 3/100 168/168 [==============================] - 0s 89us/step - loss: 0.6278 - accuracy: 0.6429 - val_loss: 0.6804 - val_accuracy: 0.5789 Epoch 4/100 168/168 [==============================] - 0s 77us/step - loss: 0.5998 - accuracy: 0.7143 - val_loss: 0.7046 - val_accuracy: 0.6316 Epoch 5/100 168/168 [==============================] - 0s 65us/step - loss: 0.5769 - accuracy: 0.7321 - val_loss: 0.7229 - val_accuracy: 0.6316 Epoch 6/100 168/168 [==============================] - 0s 77us/step - loss: 0.5672 - accuracy: 0.7381 - val_loss: 0.7281 - val_accuracy: 0.6491 Epoch 7/100 168/168 [==============================] - 0s 77us/step - loss: 0.5561 - accuracy: 0.7679 - val_loss: 0.7289 - val_accuracy: 0.6667 Epoch 8/100 168/168 [==============================] - 0s 65us/step - loss: 0.5490 - accuracy: 0.7679 - val_loss: 0.7377 - val_accuracy: 0.6667 Epoch 9/100 168/168 [==============================] - 0s 65us/step - loss: 0.5437 - accuracy: 0.7738 - val_loss: 0.7515 - val_accuracy: 0.6491 Epoch 10/100 168/168 [==============================] - 0s 59us/step - loss: 0.5371 - accuracy: 0.7738 - val_loss: 0.7648 - val_accuracy: 0.6140 Epoch 11/100 168/168 [==============================] - 0s 59us/step - loss: 0.5354 - accuracy: 0.7917 - val_loss: 0.7719 - val_accuracy: 0.6140 Epoch 12/100 168/168 [==============================] - 0s 59us/step - loss: 0.5319 - accuracy: 0.8036 - val_loss: 0.7592 - val_accuracy: 0.6316 Epoch 13/100 168/168 [==============================] - 0s 65us/step - loss: 0.5290 - accuracy: 0.7798 - val_loss: 0.7525 - val_accuracy: 0.6491 Epoch 14/100 168/168 [==============================] - 0s 65us/step - loss: 0.5266 - accuracy: 0.7917 - val_loss: 0.7563 - val_accuracy: 0.6491 Epoch 15/100 168/168 [==============================] - 0s 65us/step - loss: 0.5245 - accuracy: 0.8036 - val_loss: 0.7535 - val_accuracy: 0.6842 Epoch 16/100 168/168 [==============================] - 0s 65us/step - loss: 0.5211 - accuracy: 0.7798 - val_loss: 0.7572 - val_accuracy: 0.6842 Epoch 17/100 168/168 [==============================] - 0s 71us/step - loss: 0.5179 - accuracy: 0.7917 - val_loss: 0.7595 - val_accuracy: 0.6667 Epoch 18/100 168/168 [==============================] - 0s 59us/step - loss: 0.5160 - accuracy: 0.7976 - val_loss: 0.7636 - val_accuracy: 0.6667 Epoch 19/100 168/168 [==============================] - 0s 65us/step - loss: 0.5133 - accuracy: 0.7798 - val_loss: 0.7590 - val_accuracy: 0.6667 Epoch 20/100 168/168 [==============================] - 0s 65us/step - loss: 0.5100 - accuracy: 0.7917 - val_loss: 0.7540 - val_accuracy: 0.6667 Epoch 21/100 168/168 [==============================] - 0s 59us/step - loss: 0.5073 - accuracy: 0.7917 - val_loss: 0.7603 - val_accuracy: 0.6667 Epoch 22/100 168/168 [==============================] - 0s 65us/step - loss: 0.5060 - accuracy: 0.7857 - val_loss: 0.7696 - val_accuracy: 0.6491 Epoch 23/100 168/168 [==============================] - 0s 65us/step - loss: 0.5016 - accuracy: 0.8095 - val_loss: 0.7726 - val_accuracy: 0.6316 Epoch 24/100 168/168 [==============================] - 0s 65us/step - loss: 0.5002 - accuracy: 0.7976 - val_loss: 0.7699 - val_accuracy: 0.6667 Epoch 25/100 168/168 [==============================] - 0s 65us/step - loss: 0.4978 - accuracy: 0.7976 - val_loss: 0.7785 - val_accuracy: 0.6667 Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 26/100 168/168 [==============================] - 0s 59us/step - loss: 0.4944 - accuracy: 0.8036 - val_loss: 0.7787 - val_accuracy: 0.6842 Epoch 27/100 168/168 [==============================] - 0s 71us/step - loss: 0.4930 - accuracy: 0.7917 - val_loss: 0.7802 - val_accuracy: 0.6667 Epoch 28/100 168/168 [==============================] - 0s 101us/step - loss: 0.4907 - accuracy: 0.8036 - val_loss: 0.7801 - val_accuracy: 0.6667 Epoch 29/100 168/168 [==============================] - 0s 71us/step - loss: 0.4896 - accuracy: 0.7976 - val_loss: 0.7687 - val_accuracy: 0.6667 Epoch 30/100 168/168 [==============================] - 0s 71us/step - loss: 0.4882 - accuracy: 0.7917 - val_loss: 0.7618 - val_accuracy: 0.6667 Epoch 31/100 168/168 [==============================] - 0s 71us/step - loss: 0.4868 - accuracy: 0.7976 - val_loss: 0.7595 - val_accuracy: 0.6667 Epoch 32/100 168/168 [==============================] - 0s 71us/step - loss: 0.4847 - accuracy: 0.7917 - val_loss: 0.7590 - val_accuracy: 0.6667 Epoch 33/100 168/168 [==============================] - 0s 59us/step - loss: 0.4828 - accuracy: 0.7976 - val_loss: 0.7548 - val_accuracy: 0.6667 Epoch 34/100 168/168 [==============================] - 0s 77us/step - loss: 0.4806 - accuracy: 0.7976 - val_loss: 0.7538 - val_accuracy: 0.6667 Epoch 35/100 168/168 [==============================] - 0s 77us/step - loss: 0.4791 - accuracy: 0.8155 - val_loss: 0.7565 - val_accuracy: 0.6667 Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 36/100 168/168 [==============================] - 0s 119us/step - loss: 0.4771 - accuracy: 0.8155 - val_loss: 0.7576 - val_accuracy: 0.6667 Epoch 37/100 168/168 [==============================] - 0s 71us/step - loss: 0.4765 - accuracy: 0.8155 - val_loss: 0.7602 - val_accuracy: 0.6667 Epoch 38/100 168/168 [==============================] - 0s 77us/step - loss: 0.4759 - accuracy: 0.8155 - val_loss: 0.7621 - val_accuracy: 0.6667 Epoch 39/100 168/168 [==============================] - 0s 71us/step - loss: 0.4749 - accuracy: 0.8155 - val_loss: 0.7638 - val_accuracy: 0.6667 Epoch 40/100 168/168 [==============================] - 0s 65us/step - loss: 0.4738 - accuracy: 0.8155 - val_loss: 0.7652 - val_accuracy: 0.6667 Epoch 41/100 168/168 [==============================] - 0s 71us/step - loss: 0.4728 - accuracy: 0.8155 - val_loss: 0.7650 - val_accuracy: 0.6667 Epoch 42/100 168/168 [==============================] - 0s 65us/step - loss: 0.4723 - accuracy: 0.8155 - val_loss: 0.7694 - val_accuracy: 0.6667 Epoch 43/100 168/168 [==============================] - 0s 65us/step - loss: 0.4712 - accuracy: 0.8214 - val_loss: 0.7701 - val_accuracy: 0.6667 Epoch 44/100 168/168 [==============================] - 0s 65us/step - loss: 0.4705 - accuracy: 0.8214 - val_loss: 0.7686 - val_accuracy: 0.6667 Epoch 45/100 168/168 [==============================] - 0s 65us/step - loss: 0.4691 - accuracy: 0.8214 - val_loss: 0.7664 - val_accuracy: 0.6667 Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 46/100 168/168 [==============================] - 0s 65us/step - loss: 0.4682 - accuracy: 0.8214 - val_loss: 0.7636 - val_accuracy: 0.6667 Epoch 47/100 168/168 [==============================] - 0s 71us/step - loss: 0.4678 - accuracy: 0.8214 - val_loss: 0.7618 - val_accuracy: 0.6667 Epoch 48/100 168/168 [==============================] - 0s 71us/step - loss: 0.4672 - accuracy: 0.8214 - val_loss: 0.7608 - val_accuracy: 0.6667 Epoch 49/100 168/168 [==============================] - 0s 89us/step - loss: 0.4667 - accuracy: 0.8214 - val_loss: 0.7609 - val_accuracy: 0.6667 Epoch 50/100 168/168 [==============================] - 0s 89us/step - loss: 0.4662 - accuracy: 0.8214 - val_loss: 0.7620 - val_accuracy: 0.6667 Epoch 51/100 168/168 [==============================] - 0s 65us/step - loss: 0.4658 - accuracy: 0.8214 - val_loss: 0.7614 - val_accuracy: 0.6667 Epoch 52/100 168/168 [==============================] - 0s 71us/step - loss: 0.4652 - accuracy: 0.8214 - val_loss: 0.7618 - val_accuracy: 0.6667 Epoch 53/100 168/168 [==============================] - 0s 65us/step - loss: 0.4647 - accuracy: 0.8214 - val_loss: 0.7616 - val_accuracy: 0.6667 Epoch 54/100 168/168 [==============================] - 0s 65us/step - loss: 0.4643 - accuracy: 0.8214 - val_loss: 0.7591 - val_accuracy: 0.6667 Epoch 55/100 168/168 [==============================] - 0s 65us/step - loss: 0.4638 - accuracy: 0.8214 - val_loss: 0.7577 - val_accuracy: 0.6667 Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 56/100 168/168 [==============================] - 0s 71us/step - loss: 0.4632 - accuracy: 0.8214 - val_loss: 0.7573 - val_accuracy: 0.6667 Epoch 57/100 168/168 [==============================] - 0s 65us/step - loss: 0.4630 - accuracy: 0.8214 - val_loss: 0.7570 - val_accuracy: 0.6667 Epoch 58/100 168/168 [==============================] - 0s 59us/step - loss: 0.4628 - accuracy: 0.8274 - val_loss: 0.7557 - val_accuracy: 0.6667 Epoch 59/100 168/168 [==============================] - 0s 71us/step - loss: 0.4625 - accuracy: 0.8274 - val_loss: 0.7553 - val_accuracy: 0.6667 Epoch 60/100 168/168 [==============================] - 0s 71us/step - loss: 0.4624 - accuracy: 0.8274 - val_loss: 0.7548 - val_accuracy: 0.6667 Epoch 61/100 168/168 [==============================] - 0s 125us/step - loss: 0.4621 - accuracy: 0.8274 - val_loss: 0.7555 - val_accuracy: 0.6667 Epoch 62/100 168/168 [==============================] - 0s 83us/step - loss: 0.4618 - accuracy: 0.8274 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 63/100 168/168 [==============================] - 0s 83us/step - loss: 0.4616 - accuracy: 0.8274 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 64/100 168/168 [==============================] - 0s 71us/step - loss: 0.4613 - accuracy: 0.8274 - val_loss: 0.7565 - val_accuracy: 0.6667 Epoch 65/100 168/168 [==============================] - 0s 65us/step - loss: 0.4611 - accuracy: 0.8274 - val_loss: 0.7565 - val_accuracy: 0.6667 Epoch 00065: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 66/100 168/168 [==============================] - 0s 65us/step - loss: 0.4609 - accuracy: 0.8274 - val_loss: 0.7561 - val_accuracy: 0.6667 Epoch 67/100 168/168 [==============================] - 0s 65us/step - loss: 0.4608 - accuracy: 0.8274 - val_loss: 0.7560 - val_accuracy: 0.6667 Epoch 68/100 168/168 [==============================] - 0s 65us/step - loss: 0.4606 - accuracy: 0.8274 - val_loss: 0.7560 - val_accuracy: 0.6667 Epoch 69/100 168/168 [==============================] - 0s 65us/step - loss: 0.4605 - accuracy: 0.8274 - val_loss: 0.7559 - val_accuracy: 0.6667 Epoch 70/100 168/168 [==============================] - 0s 65us/step - loss: 0.4604 - accuracy: 0.8274 - val_loss: 0.7558 - val_accuracy: 0.6667 Epoch 71/100 168/168 [==============================] - 0s 77us/step - loss: 0.4602 - accuracy: 0.8274 - val_loss: 0.7558 - val_accuracy: 0.6667 Epoch 72/100 168/168 [==============================] - 0s 65us/step - loss: 0.4601 - accuracy: 0.8274 - val_loss: 0.7561 - val_accuracy: 0.6667 Epoch 73/100 168/168 [==============================] - 0s 71us/step - loss: 0.4600 - accuracy: 0.8274 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 74/100 168/168 [==============================] - 0s 77us/step - loss: 0.4599 - accuracy: 0.8274 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 75/100 168/168 [==============================] - 0s 65us/step - loss: 0.4598 - accuracy: 0.8274 - val_loss: 0.7560 - val_accuracy: 0.6667 Epoch 00075: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 76/100 168/168 [==============================] - 0s 65us/step - loss: 0.4596 - accuracy: 0.8274 - val_loss: 0.7561 - val_accuracy: 0.6667 Epoch 77/100 168/168 [==============================] - 0s 77us/step - loss: 0.4596 - accuracy: 0.8274 - val_loss: 0.7561 - val_accuracy: 0.6667 Epoch 78/100 168/168 [==============================] - 0s 113us/step - loss: 0.4595 - accuracy: 0.8274 - val_loss: 0.7561 - val_accuracy: 0.6667 Epoch 79/100 168/168 [==============================] - 0s 89us/step - loss: 0.4595 - accuracy: 0.8274 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 80/100 168/168 [==============================] - 0s 83us/step - loss: 0.4594 - accuracy: 0.8274 - val_loss: 0.7564 - val_accuracy: 0.6667 Epoch 81/100 168/168 [==============================] - 0s 59us/step - loss: 0.4593 - accuracy: 0.8274 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 82/100 168/168 [==============================] - 0s 65us/step - loss: 0.4593 - accuracy: 0.8274 - val_loss: 0.7565 - val_accuracy: 0.6667 Epoch 83/100 168/168 [==============================] - 0s 65us/step - loss: 0.4592 - accuracy: 0.8274 - val_loss: 0.7564 - val_accuracy: 0.6667 Epoch 84/100 168/168 [==============================] - 0s 59us/step - loss: 0.4591 - accuracy: 0.8274 - val_loss: 0.7564 - val_accuracy: 0.6667 Epoch 85/100 168/168 [==============================] - 0s 59us/step - loss: 0.4591 - accuracy: 0.8274 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 00085: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 86/100 168/168 [==============================] - 0s 59us/step - loss: 0.4590 - accuracy: 0.8214 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 87/100 168/168 [==============================] - 0s 71us/step - loss: 0.4590 - accuracy: 0.8214 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 88/100 168/168 [==============================] - 0s 65us/step - loss: 0.4590 - accuracy: 0.8214 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 89/100 168/168 [==============================] - 0s 59us/step - loss: 0.4589 - accuracy: 0.8214 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 90/100 168/168 [==============================] - 0s 83us/step - loss: 0.4589 - accuracy: 0.8214 - val_loss: 0.7567 - val_accuracy: 0.6667 Epoch 91/100 168/168 [==============================] - 0s 83us/step - loss: 0.4589 - accuracy: 0.8214 - val_loss: 0.7566 - val_accuracy: 0.6667 Epoch 92/100 168/168 [==============================] - 0s 77us/step - loss: 0.4588 - accuracy: 0.8214 - val_loss: 0.7567 - val_accuracy: 0.6667 Epoch 93/100 168/168 [==============================] - 0s 65us/step - loss: 0.4588 - accuracy: 0.8214 - val_loss: 0.7567 - val_accuracy: 0.6667 Epoch 94/100 168/168 [==============================] - 0s 65us/step - loss: 0.4588 - accuracy: 0.8214 - val_loss: 0.7567 - val_accuracy: 0.6667 Epoch 95/100 168/168 [==============================] - 0s 65us/step - loss: 0.4587 - accuracy: 0.8214 - val_loss: 0.7565 - val_accuracy: 0.6667 Epoch 00095: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 96/100 168/168 [==============================] - 0s 71us/step - loss: 0.4587 - accuracy: 0.8214 - val_loss: 0.7564 - val_accuracy: 0.6667 Epoch 97/100 168/168 [==============================] - 0s 59us/step - loss: 0.4587 - accuracy: 0.8214 - val_loss: 0.7564 - val_accuracy: 0.6667 Epoch 98/100 168/168 [==============================] - 0s 65us/step - loss: 0.4587 - accuracy: 0.8214 - val_loss: 0.7563 - val_accuracy: 0.6667 Epoch 99/100 168/168 [==============================] - 0s 65us/step - loss: 0.4587 - accuracy: 0.8214 - val_loss: 0.7562 - val_accuracy: 0.6667 Epoch 100/100 168/168 [==============================] - 0s 71us/step - loss: 0.4586 - accuracy: 0.8214 - val_loss: 0.7561 - val_accuracy: 0.6667
In [396]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [397]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
57/57 [==============================] - 0s 53us/step test loss: 0.756120643071961, test accuracy: 0.6666666865348816
In [398]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.5753246753246753
In [399]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.19479553903345737
KMeans¶
In [357]:
X
Out[357]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | |
|---|---|---|---|---|---|---|
| 0 | 1.221298 | -0.131733 | -0.299437 | -1.504299 | -0.981450 | 0.118501 |
| 1 | -0.401407 | -1.423353 | 0.879221 | 0.761177 | -0.141123 | -1.312558 |
| 2 | -0.336789 | -0.749510 | 0.197427 | 0.350093 | -0.148137 | 0.191187 |
| 3 | -0.785803 | 0.323072 | 0.152856 | -0.630916 | 1.431384 | -1.260729 |
| 4 | -0.879329 | 0.391910 | 0.348949 | -0.626137 | 1.401315 | -0.417223 |
| 5 | -1.122300 | 0.270282 | 0.525297 | 1.043848 | -0.500903 | 0.121388 |
| 6 | 1.854801 | -0.110375 | 0.714529 | -0.774181 | 0.550772 | -0.030796 |
| 7 | 1.233933 | 0.235407 | 0.220324 | -0.416248 | 0.185500 | 0.429491 |
| 8 | 0.870319 | 0.678474 | 0.499988 | 1.108991 | 1.281285 | 0.734961 |
| 9 | 0.390986 | 0.860360 | -1.381945 | 0.173292 | -0.891547 | -0.692921 |
| 10 | 0.746873 | 1.087314 | 0.138873 | 0.673665 | 0.999100 | 1.072527 |
| 11 | 0.794479 | 0.967314 | -0.363499 | 1.122351 | 0.135802 | -0.719195 |
| 12 | -1.413209 | -0.136961 | -0.371584 | 0.036112 | -0.471990 | -1.250545 |
| 13 | -0.072759 | -1.149988 | 1.952271 | 0.500676 | 0.052506 | -1.006895 |
| 14 | -1.273932 | -0.022476 | -0.393398 | 0.422307 | -1.130763 | -0.797664 |
| 15 | -1.343760 | -0.913734 | 0.077520 | -0.431201 | -1.914695 | 0.581853 |
| 16 | -0.984733 | -0.428093 | 1.220123 | -0.685684 | 1.139966 | 0.712742 |
| 17 | 0.280303 | 1.062737 | -0.783356 | -0.172690 | -0.675275 | 0.261145 |
| 18 | 0.000660 | 1.240654 | -0.919850 | -0.619755 | -0.483109 | -0.722220 |
| 19 | 0.174949 | 1.113514 | -2.625056 | -0.681779 | -0.763682 | 0.031420 |
| 20 | -0.183363 | -1.557702 | -0.979966 | 0.553109 | 1.161043 | -0.644240 |
| 21 | -0.171420 | -1.728398 | -0.653886 | -0.361851 | 0.584439 | -0.772678 |
| 22 | -0.120605 | -1.190295 | 1.627818 | 0.127251 | -0.575228 | 0.475040 |
| 23 | -1.074141 | 1.086592 | 0.969446 | 0.102117 | 0.044168 | 1.096704 |
| 24 | -1.016635 | 1.061833 | 0.981137 | -0.047605 | -0.063650 | 0.778928 |
| 25 | -1.093370 | 1.015536 | -0.193033 | -0.486717 | -0.763605 | -0.977680 |
| 26 | -0.428676 | 1.178009 | 1.290983 | 0.772016 | 1.623629 | 0.867341 |
| 27 | -1.196386 | -0.495161 | 0.531435 | 0.385220 | -1.383020 | 0.795049 |
| 28 | 0.866731 | 0.412772 | 1.289048 | -0.014727 | 1.077496 | -0.617061 |
| 29 | 1.237442 | 0.582034 | 1.478319 | -0.565057 | 0.802127 | -0.509015 |
| ... | ... | ... | ... | ... | ... | ... |
| 195 | -0.603701 | 0.935409 | -0.419589 | -0.931752 | 0.907517 | -1.018120 |
| 196 | 1.708997 | -0.221303 | -1.454810 | -0.881563 | -0.670232 | -0.206469 |
| 197 | -0.120947 | 0.962072 | -0.135798 | 1.668524 | 0.852748 | -1.052456 |
| 198 | 1.023034 | 0.904205 | -0.425604 | 1.452149 | 1.440979 | 2.026447 |
| 199 | 1.607139 | 1.023377 | -0.668664 | -0.630504 | 0.512795 | 1.087292 |
| 200 | 1.108675 | 0.450827 | -1.313634 | -0.450630 | -1.386651 | 1.571188 |
| 201 | 0.806904 | 1.114638 | 0.335775 | 0.794859 | 0.028131 | 1.383505 |
| 202 | 1.126699 | 1.174832 | -0.674497 | 0.392635 | -1.533629 | 0.824473 |
| 203 | -0.345663 | -1.373739 | 1.109525 | 0.377449 | -2.292182 | -1.513942 |
| 204 | 1.158812 | 0.165993 | -1.074239 | 1.512949 | 2.035898 | 0.995373 |
| 205 | 1.365396 | -0.199409 | -0.349417 | -2.286552 | -1.926263 | 0.106827 |
| 206 | 0.988164 | -1.144285 | -0.272909 | -0.868581 | -2.124224 | 1.273870 |
| 207 | 0.905347 | -0.717727 | 0.050759 | 0.376767 | -0.593122 | 0.072830 |
| 208 | 0.749668 | 1.003858 | -0.003342 | 2.104938 | 0.631976 | 0.706610 |
| 209 | 1.094646 | 0.741328 | -0.142645 | 2.175202 | 1.046907 | 1.820650 |
| 210 | 0.893047 | 0.990481 | 0.297007 | 2.573965 | 0.840532 | 0.246122 |
| 211 | 2.012667 | 0.112112 | 2.554833 | 0.422693 | 0.504618 | -0.513686 |
| 212 | 0.509494 | -0.082608 | 1.308021 | -0.752580 | 0.394880 | -1.613007 |
| 213 | 0.505972 | 0.224521 | 1.805274 | -0.533802 | 1.144542 | -2.601310 |
| 214 | 1.135681 | 1.126084 | 0.303609 | 1.072045 | 0.556683 | 1.593221 |
| 215 | 0.894848 | 1.251553 | 0.035370 | 1.132716 | 1.078850 | -0.087268 |
| 216 | 0.990624 | 1.229073 | 0.307033 | 1.158121 | 1.115168 | -0.568339 |
| 217 | -1.693745 | 0.917833 | -1.767095 | -0.013187 | 0.902548 | -0.580976 |
| 218 | -1.788316 | 1.075356 | -1.823314 | 0.354994 | 1.353021 | -0.863177 |
| 219 | -1.682127 | 0.743941 | -1.291767 | 0.306169 | 1.596294 | -0.005674 |
| 220 | 0.705066 | 0.687717 | -1.639219 | 1.045094 | -0.345182 | 3.118994 |
| 221 | 0.520886 | 1.211743 | 0.887971 | 1.035591 | 0.420222 | 1.151952 |
| 222 | 0.456500 | -1.350072 | -0.027792 | -0.269632 | -1.081328 | 0.764550 |
| 223 | -0.648368 | 0.201861 | 0.483292 | -0.672521 | 0.434345 | -0.365001 |
| 224 | 0.650931 | -1.322808 | 0.580194 | 0.873078 | -0.583450 | -0.803746 |
225 rows × 6 columns
In [358]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[358]:
[1350.0, 1124.435731037261, 969.7111524654798, 861.1739982448028, 779.024220761341, 706.2330662595951, 649.2198311716684, 599.2097480238398, 565.7104737024936, 525.1010514512898, 497.51074255236824, 479.1767694058344, 448.31267298836974, 436.24565823492435]
In [359]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[359]:
[<matplotlib.lines.Line2D at 0x1e8322a9160>]
K=3
In [360]:
kmeans_tc = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_tc.fit(X)
Out[360]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [361]:
kmeans_tc.labels_
Out[361]:
array([0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 2, 1, 2, 0, 2, 2,
2, 2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2,
2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1,
0, 0, 0, 2, 2, 2, 1, 0, 2, 2, 2, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0,
1, 1, 2, 1, 1, 0, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0,
1, 1, 0, 1, 2, 2, 2, 1, 0, 1, 1, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 2,
2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 1, 2, 0, 1, 2, 0, 2, 1,
2, 1, 1, 2, 2, 1, 0, 2, 1, 0, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2,
2, 0, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 0, 1,
1, 1, 0, 1, 1, 2, 1, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2,
1, 1, 0, 2, 2])
In [362]:
clusters_tc = kmeans_tc.predict(X)
clusters_tc
Out[362]:
array([0, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 2, 1, 2, 0, 2, 2,
2, 2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2,
2, 2, 0, 0, 0, 2, 0, 2, 1, 1, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 1,
0, 0, 0, 2, 2, 2, 1, 0, 2, 2, 2, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0,
1, 1, 2, 1, 1, 0, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 0,
1, 1, 0, 1, 2, 2, 2, 1, 0, 1, 1, 2, 1, 1, 0, 0, 0, 1, 1, 1, 1, 2,
2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 1, 2, 0, 1, 2, 0, 2, 1,
2, 1, 1, 2, 2, 1, 0, 2, 1, 0, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2,
2, 0, 2, 1, 1, 0, 0, 0, 1, 1, 2, 2, 1, 2, 0, 1, 1, 1, 2, 2, 0, 1,
1, 1, 0, 1, 1, 2, 1, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2,
1, 1, 0, 2, 2])
In [363]:
X.loc[:,'Cluster'] = clusters_tc
X.loc[:,'chosen'] = list(y)
In [364]:
X
Out[364]:
| tonalcentroidfiles_1 | tonalcentroidfiles_2 | tonalcentroidfiles_3 | tonalcentroidfiles_4 | tonalcentroidfiles_5 | tonalcentroidfiles_6 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1.221298 | -0.131733 | -0.299437 | -1.504299 | -0.981450 | 0.118501 | 0 | 0 |
| 1 | -0.401407 | -1.423353 | 0.879221 | 0.761177 | -0.141123 | -1.312558 | 2 | 0 |
| 2 | -0.336789 | -0.749510 | 0.197427 | 0.350093 | -0.148137 | 0.191187 | 2 | 0 |
| 3 | -0.785803 | 0.323072 | 0.152856 | -0.630916 | 1.431384 | -1.260729 | 2 | 0 |
| 4 | -0.879329 | 0.391910 | 0.348949 | -0.626137 | 1.401315 | -0.417223 | 2 | 0 |
| 5 | -1.122300 | 0.270282 | 0.525297 | 1.043848 | -0.500903 | 0.121388 | 2 | 0 |
| 6 | 1.854801 | -0.110375 | 0.714529 | -0.774181 | 0.550772 | -0.030796 | 0 | 0 |
| 7 | 1.233933 | 0.235407 | 0.220324 | -0.416248 | 0.185500 | 0.429491 | 1 | 0 |
| 8 | 0.870319 | 0.678474 | 0.499988 | 1.108991 | 1.281285 | 0.734961 | 1 | 0 |
| 9 | 0.390986 | 0.860360 | -1.381945 | 0.173292 | -0.891547 | -0.692921 | 1 | 0 |
| 10 | 0.746873 | 1.087314 | 0.138873 | 0.673665 | 0.999100 | 1.072527 | 1 | 0 |
| 11 | 0.794479 | 0.967314 | -0.363499 | 1.122351 | 0.135802 | -0.719195 | 1 | 0 |
| 12 | -1.413209 | -0.136961 | -0.371584 | 0.036112 | -0.471990 | -1.250545 | 2 | 0 |
| 13 | -0.072759 | -1.149988 | 1.952271 | 0.500676 | 0.052506 | -1.006895 | 2 | 0 |
| 14 | -1.273932 | -0.022476 | -0.393398 | 0.422307 | -1.130763 | -0.797664 | 2 | 0 |
| 15 | -1.343760 | -0.913734 | 0.077520 | -0.431201 | -1.914695 | 0.581853 | 0 | 0 |
| 16 | -0.984733 | -0.428093 | 1.220123 | -0.685684 | 1.139966 | 0.712742 | 2 | 0 |
| 17 | 0.280303 | 1.062737 | -0.783356 | -0.172690 | -0.675275 | 0.261145 | 1 | 0 |
| 18 | 0.000660 | 1.240654 | -0.919850 | -0.619755 | -0.483109 | -0.722220 | 2 | 0 |
| 19 | 0.174949 | 1.113514 | -2.625056 | -0.681779 | -0.763682 | 0.031420 | 0 | 0 |
| 20 | -0.183363 | -1.557702 | -0.979966 | 0.553109 | 1.161043 | -0.644240 | 2 | 0 |
| 21 | -0.171420 | -1.728398 | -0.653886 | -0.361851 | 0.584439 | -0.772678 | 2 | 0 |
| 22 | -0.120605 | -1.190295 | 1.627818 | 0.127251 | -0.575228 | 0.475040 | 2 | 0 |
| 23 | -1.074141 | 1.086592 | 0.969446 | 0.102117 | 0.044168 | 1.096704 | 2 | 0 |
| 24 | -1.016635 | 1.061833 | 0.981137 | -0.047605 | -0.063650 | 0.778928 | 2 | 0 |
| 25 | -1.093370 | 1.015536 | -0.193033 | -0.486717 | -0.763605 | -0.977680 | 2 | 0 |
| 26 | -0.428676 | 1.178009 | 1.290983 | 0.772016 | 1.623629 | 0.867341 | 1 | 0 |
| 27 | -1.196386 | -0.495161 | 0.531435 | 0.385220 | -1.383020 | 0.795049 | 2 | 0 |
| 28 | 0.866731 | 0.412772 | 1.289048 | -0.014727 | 1.077496 | -0.617061 | 2 | 0 |
| 29 | 1.237442 | 0.582034 | 1.478319 | -0.565057 | 0.802127 | -0.509015 | 2 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 195 | -0.603701 | 0.935409 | -0.419589 | -0.931752 | 0.907517 | -1.018120 | 2 | 1 |
| 196 | 1.708997 | -0.221303 | -1.454810 | -0.881563 | -0.670232 | -0.206469 | 0 | 1 |
| 197 | -0.120947 | 0.962072 | -0.135798 | 1.668524 | 0.852748 | -1.052456 | 1 | 1 |
| 198 | 1.023034 | 0.904205 | -0.425604 | 1.452149 | 1.440979 | 2.026447 | 1 | 1 |
| 199 | 1.607139 | 1.023377 | -0.668664 | -0.630504 | 0.512795 | 1.087292 | 1 | 1 |
| 200 | 1.108675 | 0.450827 | -1.313634 | -0.450630 | -1.386651 | 1.571188 | 0 | 1 |
| 201 | 0.806904 | 1.114638 | 0.335775 | 0.794859 | 0.028131 | 1.383505 | 1 | 1 |
| 202 | 1.126699 | 1.174832 | -0.674497 | 0.392635 | -1.533629 | 0.824473 | 1 | 1 |
| 203 | -0.345663 | -1.373739 | 1.109525 | 0.377449 | -2.292182 | -1.513942 | 2 | 1 |
| 204 | 1.158812 | 0.165993 | -1.074239 | 1.512949 | 2.035898 | 0.995373 | 1 | 1 |
| 205 | 1.365396 | -0.199409 | -0.349417 | -2.286552 | -1.926263 | 0.106827 | 0 | 1 |
| 206 | 0.988164 | -1.144285 | -0.272909 | -0.868581 | -2.124224 | 1.273870 | 0 | 1 |
| 207 | 0.905347 | -0.717727 | 0.050759 | 0.376767 | -0.593122 | 0.072830 | 0 | 1 |
| 208 | 0.749668 | 1.003858 | -0.003342 | 2.104938 | 0.631976 | 0.706610 | 1 | 1 |
| 209 | 1.094646 | 0.741328 | -0.142645 | 2.175202 | 1.046907 | 1.820650 | 1 | 1 |
| 210 | 0.893047 | 0.990481 | 0.297007 | 2.573965 | 0.840532 | 0.246122 | 1 | 1 |
| 211 | 2.012667 | 0.112112 | 2.554833 | 0.422693 | 0.504618 | -0.513686 | 1 | 1 |
| 212 | 0.509494 | -0.082608 | 1.308021 | -0.752580 | 0.394880 | -1.613007 | 2 | 1 |
| 213 | 0.505972 | 0.224521 | 1.805274 | -0.533802 | 1.144542 | -2.601310 | 2 | 1 |
| 214 | 1.135681 | 1.126084 | 0.303609 | 1.072045 | 0.556683 | 1.593221 | 1 | 1 |
| 215 | 0.894848 | 1.251553 | 0.035370 | 1.132716 | 1.078850 | -0.087268 | 1 | 1 |
| 216 | 0.990624 | 1.229073 | 0.307033 | 1.158121 | 1.115168 | -0.568339 | 1 | 1 |
| 217 | -1.693745 | 0.917833 | -1.767095 | -0.013187 | 0.902548 | -0.580976 | 2 | 1 |
| 218 | -1.788316 | 1.075356 | -1.823314 | 0.354994 | 1.353021 | -0.863177 | 2 | 1 |
| 219 | -1.682127 | 0.743941 | -1.291767 | 0.306169 | 1.596294 | -0.005674 | 2 | 1 |
| 220 | 0.705066 | 0.687717 | -1.639219 | 1.045094 | -0.345182 | 3.118994 | 1 | 1 |
| 221 | 0.520886 | 1.211743 | 0.887971 | 1.035591 | 0.420222 | 1.151952 | 1 | 1 |
| 222 | 0.456500 | -1.350072 | -0.027792 | -0.269632 | -1.081328 | 0.764550 | 0 | 1 |
| 223 | -0.648368 | 0.201861 | 0.483292 | -0.672521 | 0.434345 | -0.365001 | 2 | 1 |
| 224 | 0.650931 | -1.322808 | 0.580194 | 0.873078 | -0.583450 | -0.803746 | 2 | 1 |
225 rows × 8 columns
In [365]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[365]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e831cecf60>
Chromagram¶
In [404]:
df_n_ps_std[0].columns
Out[404]:
Index(['durationfiles', 'rmsfiles', 'rmsmedianfiles', 'lowenergyfiles',
'ASRfiles', 'beatspectrumfiles', 'eventdensityfiles', 'tempofiles',
'pulseclarityfiles', 'zerocrossfiles', 'rolloffsfiles',
'brightnessfiles', 'spreadfiles', 'centroidfiles', 'kurtosisfiles',
'flatnessfiles', 'entropyfiles', 'mfccfiles_1', 'mfccfiles_2',
'mfccfiles_3', 'mfccfiles_4', 'mfccfiles_5', 'mfccfiles_6',
'mfccfiles_7', 'mfccfiles_8', 'mfccfiles_9', 'mfccfiles_10',
'mfccfiles_11', 'mfccfiles_12', 'mfccfiles_13', 'inharmonicityfiles',
'bestkeyfiles', 'keyclarityfiles', 'modalityfiles',
'tonalcentroidfiles_1', 'tonalcentroidfiles_2', 'tonalcentroidfiles_3',
'tonalcentroidfiles_4', 'tonalcentroidfiles_5', 'tonalcentroidfiles_6',
'chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12',
'attackslopefiles', 'attackleapfiles', 'chosen'],
dtype='object')
In [405]:
df_n_ps_std[0].columns[40:52]
Out[405]:
Index(['chromagramfiles_1', 'chromagramfiles_2', 'chromagramfiles_3',
'chromagramfiles_4', 'chromagramfiles_5', 'chromagramfiles_6',
'chromagramfiles_7', 'chromagramfiles_8', 'chromagramfiles_9',
'chromagramfiles_10', 'chromagramfiles_11', 'chromagramfiles_12'],
dtype='object')
In [406]:
df_n_ps_std_ch = [None]*len(companies)
for i in range(len(companies)):
df_n_ps_std_ch[i] = pd.DataFrame(df_n_ps_std[i].iloc[:,40:52])
df_n_ps_std_ch[i].columns=df_n_ps_std[i].columns[40:52]
df_n_ps_std_ch[0].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 372 entries, 0 to 371 Data columns (total 12 columns): chromagramfiles_1 372 non-null float64 chromagramfiles_2 372 non-null float64 chromagramfiles_3 372 non-null float64 chromagramfiles_4 372 non-null float64 chromagramfiles_5 372 non-null float64 chromagramfiles_6 372 non-null float64 chromagramfiles_7 372 non-null float64 chromagramfiles_8 372 non-null float64 chromagramfiles_9 372 non-null float64 chromagramfiles_10 372 non-null float64 chromagramfiles_11 372 non-null float64 chromagramfiles_12 372 non-null float64 dtypes: float64(12) memory usage: 35.0 KB
Arte Francés¶
ANN¶
In [407]:
X = df_n_ps_std_ch[0]
In [408]:
y = df_n_ps[0]['chosen']
In [409]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [410]:
X_train.shape
Out[410]:
(279, 12)
In [24]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [25]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [26]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [27]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (30, 30, 30), 'learning_rate_init': 0.008, 'max_iter': 100}, que permiten obtener un Accuracy de 78.49% y un Kappa del 30.33
Tiempo total: 23.83 minutos
In [411]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30,30,30]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.008
epochs = 100
In [412]:
input_tensor = Input(shape = (n0,))
In [413]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [414]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [415]:
model.summary()
Model: "model_21" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_21 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_66 (Dense) (None, 30) 390 _________________________________________________________________ dense_67 (Dense) (None, 30) 930 _________________________________________________________________ dense_68 (Dense) (None, 30) 930 _________________________________________________________________ dense_69 (Dense) (None, 1) 31 ================================================================= Total params: 2,281 Trainable params: 2,281 Non-trainable params: 0 _________________________________________________________________
In [416]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 279 samples, validate on 93 samples Epoch 1/100 279/279 [==============================] - 0s 792us/step - loss: 0.6389 - accuracy: 0.6452 - val_loss: 0.5521 - val_accuracy: 0.6882 Epoch 2/100 279/279 [==============================] - 0s 68us/step - loss: 0.5409 - accuracy: 0.7384 - val_loss: 0.5195 - val_accuracy: 0.7742 Epoch 3/100 279/279 [==============================] - 0s 54us/step - loss: 0.5159 - accuracy: 0.7634 - val_loss: 0.5163 - val_accuracy: 0.7742 Epoch 4/100 279/279 [==============================] - 0s 61us/step - loss: 0.4912 - accuracy: 0.7634 - val_loss: 0.5340 - val_accuracy: 0.7849 Epoch 5/100 279/279 [==============================] - 0s 57us/step - loss: 0.4777 - accuracy: 0.7706 - val_loss: 0.5239 - val_accuracy: 0.7634 Epoch 6/100 279/279 [==============================] - 0s 57us/step - loss: 0.4586 - accuracy: 0.7742 - val_loss: 0.5071 - val_accuracy: 0.7634 Epoch 7/100 279/279 [==============================] - 0s 57us/step - loss: 0.4379 - accuracy: 0.7885 - val_loss: 0.5330 - val_accuracy: 0.7742 Epoch 8/100 279/279 [==============================] - 0s 57us/step - loss: 0.4370 - accuracy: 0.7993 - val_loss: 0.5240 - val_accuracy: 0.7849 Epoch 9/100 279/279 [==============================] - 0s 57us/step - loss: 0.3991 - accuracy: 0.8208 - val_loss: 0.4966 - val_accuracy: 0.7849 Epoch 10/100 279/279 [==============================] - 0s 54us/step - loss: 0.3763 - accuracy: 0.8459 - val_loss: 0.5227 - val_accuracy: 0.7742 Epoch 11/100 279/279 [==============================] - 0s 61us/step - loss: 0.3536 - accuracy: 0.8674 - val_loss: 0.5219 - val_accuracy: 0.7849 Epoch 12/100 279/279 [==============================] - 0s 57us/step - loss: 0.3131 - accuracy: 0.8781 - val_loss: 0.5182 - val_accuracy: 0.7527 Epoch 13/100 279/279 [==============================] - 0s 61us/step - loss: 0.2881 - accuracy: 0.8817 - val_loss: 0.5220 - val_accuracy: 0.7849 Epoch 14/100 279/279 [==============================] - 0s 57us/step - loss: 0.2619 - accuracy: 0.8961 - val_loss: 0.5317 - val_accuracy: 0.7742 Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.004000000189989805. Epoch 15/100 279/279 [==============================] - 0s 61us/step - loss: 0.2267 - accuracy: 0.9211 - val_loss: 0.5444 - val_accuracy: 0.7742 Epoch 16/100 279/279 [==============================] - 0s 57us/step - loss: 0.2121 - accuracy: 0.9247 - val_loss: 0.5642 - val_accuracy: 0.7742 Epoch 17/100 279/279 [==============================] - 0s 57us/step - loss: 0.1956 - accuracy: 0.9319 - val_loss: 0.5427 - val_accuracy: 0.7742 Epoch 18/100 279/279 [==============================] - 0s 61us/step - loss: 0.1754 - accuracy: 0.9427 - val_loss: 0.5455 - val_accuracy: 0.7849 Epoch 19/100 279/279 [==============================] - 0s 57us/step - loss: 0.1654 - accuracy: 0.9570 - val_loss: 0.5611 - val_accuracy: 0.7957 Epoch 20/100 279/279 [==============================] - 0s 64us/step - loss: 0.1513 - accuracy: 0.9642 - val_loss: 0.6203 - val_accuracy: 0.7742 Epoch 21/100 279/279 [==============================] - 0s 57us/step - loss: 0.1370 - accuracy: 0.9642 - val_loss: 0.5838 - val_accuracy: 0.7849 Epoch 22/100 279/279 [==============================] - 0s 57us/step - loss: 0.1285 - accuracy: 0.9642 - val_loss: 0.5995 - val_accuracy: 0.7849 Epoch 23/100 279/279 [==============================] - 0s 68us/step - loss: 0.1139 - accuracy: 0.9785 - val_loss: 0.6266 - val_accuracy: 0.7849 Epoch 24/100 279/279 [==============================] - 0s 75us/step - loss: 0.1090 - accuracy: 0.9857 - val_loss: 0.6823 - val_accuracy: 0.7634 Epoch 25/100 279/279 [==============================] - 0s 61us/step - loss: 0.1047 - accuracy: 0.9785 - val_loss: 0.6614 - val_accuracy: 0.7527 Epoch 26/100 279/279 [==============================] - 0s 90us/step - loss: 0.0932 - accuracy: 0.9821 - val_loss: 0.7107 - val_accuracy: 0.7527 Epoch 27/100 279/279 [==============================] - 0s 64us/step - loss: 0.0814 - accuracy: 0.9928 - val_loss: 0.6746 - val_accuracy: 0.7742 Epoch 28/100 279/279 [==============================] - 0s 64us/step - loss: 0.0705 - accuracy: 0.9892 - val_loss: 0.7344 - val_accuracy: 0.7634 Epoch 29/100 279/279 [==============================] - 0s 82us/step - loss: 0.0669 - accuracy: 0.9928 - val_loss: 0.7636 - val_accuracy: 0.7527 Epoch 00029: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 30/100 279/279 [==============================] - 0s 64us/step - loss: 0.0597 - accuracy: 0.9964 - val_loss: 0.7633 - val_accuracy: 0.7634 Epoch 31/100 279/279 [==============================] - 0s 64us/step - loss: 0.0557 - accuracy: 0.9964 - val_loss: 0.7586 - val_accuracy: 0.7742 Epoch 32/100 279/279 [==============================] - 0s 57us/step - loss: 0.0525 - accuracy: 0.9964 - val_loss: 0.7917 - val_accuracy: 0.7634 Epoch 33/100 279/279 [==============================] - 0s 82us/step - loss: 0.0516 - accuracy: 0.9892 - val_loss: 0.7975 - val_accuracy: 0.7849 Epoch 34/100 279/279 [==============================] - 0s 97us/step - loss: 0.0449 - accuracy: 0.9964 - val_loss: 0.7797 - val_accuracy: 0.7634 Epoch 35/100 279/279 [==============================] - 0s 90us/step - loss: 0.0449 - accuracy: 0.9964 - val_loss: 0.8031 - val_accuracy: 0.7742 Epoch 36/100 279/279 [==============================] - 0s 79us/step - loss: 0.0424 - accuracy: 0.9964 - val_loss: 0.8336 - val_accuracy: 0.7742 Epoch 37/100 279/279 [==============================] - 0s 64us/step - loss: 0.0408 - accuracy: 0.9964 - val_loss: 0.7955 - val_accuracy: 0.7742 Epoch 38/100 279/279 [==============================] - 0s 61us/step - loss: 0.0399 - accuracy: 0.9964 - val_loss: 0.8462 - val_accuracy: 0.7742 Epoch 39/100 279/279 [==============================] - 0s 61us/step - loss: 0.0343 - accuracy: 0.9964 - val_loss: 0.8379 - val_accuracy: 0.7742 Epoch 00039: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 40/100 279/279 [==============================] - 0s 57us/step - loss: 0.0333 - accuracy: 0.9964 - val_loss: 0.8493 - val_accuracy: 0.7849 Epoch 41/100 279/279 [==============================] - 0s 57us/step - loss: 0.0318 - accuracy: 0.9964 - val_loss: 0.8589 - val_accuracy: 0.7849 Epoch 42/100 279/279 [==============================] - 0s 57us/step - loss: 0.0307 - accuracy: 0.9964 - val_loss: 0.8626 - val_accuracy: 0.7742 Epoch 43/100 279/279 [==============================] - 0s 61us/step - loss: 0.0296 - accuracy: 0.9964 - val_loss: 0.8739 - val_accuracy: 0.7634 Epoch 44/100 279/279 [==============================] - 0s 57us/step - loss: 0.0290 - accuracy: 0.9964 - val_loss: 0.8782 - val_accuracy: 0.7742 Epoch 45/100 279/279 [==============================] - 0s 57us/step - loss: 0.0282 - accuracy: 0.9964 - val_loss: 0.8787 - val_accuracy: 0.7634 Epoch 46/100 279/279 [==============================] - 0s 64us/step - loss: 0.0273 - accuracy: 0.9964 - val_loss: 0.8910 - val_accuracy: 0.7742 Epoch 47/100 279/279 [==============================] - 0s 61us/step - loss: 0.0270 - accuracy: 0.9964 - val_loss: 0.8854 - val_accuracy: 0.7742 Epoch 48/100 279/279 [==============================] - 0s 100us/step - loss: 0.0262 - accuracy: 0.9964 - val_loss: 0.8989 - val_accuracy: 0.7634 Epoch 49/100 279/279 [==============================] - 0s 68us/step - loss: 0.0249 - accuracy: 0.9964 - val_loss: 0.9010 - val_accuracy: 0.7742 Epoch 00049: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 50/100 279/279 [==============================] - 0s 64us/step - loss: 0.0243 - accuracy: 0.9964 - val_loss: 0.9060 - val_accuracy: 0.7742 Epoch 51/100 279/279 [==============================] - 0s 61us/step - loss: 0.0239 - accuracy: 0.9964 - val_loss: 0.9030 - val_accuracy: 0.7634 Epoch 52/100 279/279 [==============================] - 0s 61us/step - loss: 0.0236 - accuracy: 0.9964 - val_loss: 0.9014 - val_accuracy: 0.7634 Epoch 53/100 279/279 [==============================] - 0s 72us/step - loss: 0.0230 - accuracy: 0.9964 - val_loss: 0.9147 - val_accuracy: 0.7742 Epoch 54/100 279/279 [==============================] - 0s 61us/step - loss: 0.0228 - accuracy: 0.9964 - val_loss: 0.9206 - val_accuracy: 0.7742 Epoch 55/100 279/279 [==============================] - 0s 61us/step - loss: 0.0223 - accuracy: 0.9964 - val_loss: 0.9178 - val_accuracy: 0.7742 Epoch 56/100 279/279 [==============================] - 0s 61us/step - loss: 0.0218 - accuracy: 0.9964 - val_loss: 0.9186 - val_accuracy: 0.7742 Epoch 57/100 279/279 [==============================] - 0s 57us/step - loss: 0.0216 - accuracy: 0.9964 - val_loss: 0.9237 - val_accuracy: 0.7742 Epoch 58/100 279/279 [==============================] - 0s 82us/step - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.9231 - val_accuracy: 0.7742 Epoch 59/100 279/279 [==============================] - 0s 68us/step - loss: 0.0210 - accuracy: 1.0000 - val_loss: 0.9316 - val_accuracy: 0.7742 Epoch 00059: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 60/100 279/279 [==============================] - 0s 57us/step - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.9325 - val_accuracy: 0.7742 Epoch 61/100 279/279 [==============================] - 0s 61us/step - loss: 0.0204 - accuracy: 1.0000 - val_loss: 0.9334 - val_accuracy: 0.7634 Epoch 62/100 279/279 [==============================] - 0s 57us/step - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.9332 - val_accuracy: 0.7634 Epoch 63/100 279/279 [==============================] - 0s 61us/step - loss: 0.0201 - accuracy: 1.0000 - val_loss: 0.9352 - val_accuracy: 0.7634 Epoch 64/100 279/279 [==============================] - 0s 61us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.9350 - val_accuracy: 0.7634 Epoch 65/100 279/279 [==============================] - 0s 61us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.9396 - val_accuracy: 0.7634 Epoch 66/100 279/279 [==============================] - 0s 61us/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.9378 - val_accuracy: 0.7634 Epoch 67/100 279/279 [==============================] - 0s 68us/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 0.9396 - val_accuracy: 0.7634 Epoch 68/100 279/279 [==============================] - 0s 86us/step - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.9423 - val_accuracy: 0.7634 Epoch 69/100 279/279 [==============================] - 0s 57us/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.9436 - val_accuracy: 0.7634 Epoch 00069: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 70/100 279/279 [==============================] - 0s 64us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.9444 - val_accuracy: 0.7634 Epoch 71/100 279/279 [==============================] - 0s 57us/step - loss: 0.0189 - accuracy: 1.0000 - val_loss: 0.9445 - val_accuracy: 0.7634 Epoch 72/100 279/279 [==============================] - 0s 57us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.9453 - val_accuracy: 0.7634 Epoch 73/100 279/279 [==============================] - 0s 61us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.9446 - val_accuracy: 0.7634 Epoch 74/100 279/279 [==============================] - 0s 57us/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.9460 - val_accuracy: 0.7634 Epoch 75/100 279/279 [==============================] - 0s 61us/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 0.9471 - val_accuracy: 0.7634 Epoch 76/100 279/279 [==============================] - 0s 61us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.9490 - val_accuracy: 0.7634 Epoch 77/100 279/279 [==============================] - 0s 64us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.9488 - val_accuracy: 0.7634 Epoch 78/100 279/279 [==============================] - 0s 57us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.9503 - val_accuracy: 0.7634 Epoch 79/100 279/279 [==============================] - 0s 61us/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.9509 - val_accuracy: 0.7634 Epoch 00079: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 80/100 279/279 [==============================] - 0s 68us/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 0.9515 - val_accuracy: 0.7634 Epoch 81/100 279/279 [==============================] - 0s 61us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.9519 - val_accuracy: 0.7634 Epoch 82/100 279/279 [==============================] - 0s 64us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.9524 - val_accuracy: 0.7634 Epoch 83/100 279/279 [==============================] - 0s 90us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.9528 - val_accuracy: 0.7634 Epoch 84/100 279/279 [==============================] - 0s 90us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.9532 - val_accuracy: 0.7634 Epoch 85/100 279/279 [==============================] - 0s 90us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.9536 - val_accuracy: 0.7634 Epoch 86/100 279/279 [==============================] - 0s 93us/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 0.9537 - val_accuracy: 0.7634 Epoch 87/100 279/279 [==============================] - 0s 86us/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.9538 - val_accuracy: 0.7634 Epoch 88/100 279/279 [==============================] - 0s 64us/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.9538 - val_accuracy: 0.7634 Epoch 89/100 279/279 [==============================] - 0s 61us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.9535 - val_accuracy: 0.7634 Epoch 00089: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 90/100 279/279 [==============================] - 0s 61us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.9540 - val_accuracy: 0.7634 Epoch 91/100 279/279 [==============================] - 0s 68us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.9542 - val_accuracy: 0.7634 Epoch 92/100 279/279 [==============================] - 0s 54us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.9544 - val_accuracy: 0.7634 Epoch 93/100 279/279 [==============================] - 0s 61us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.9549 - val_accuracy: 0.7634 Epoch 94/100 279/279 [==============================] - 0s 64us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.9548 - val_accuracy: 0.7634 Epoch 95/100 279/279 [==============================] - 0s 57us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.9552 - val_accuracy: 0.7634 Epoch 96/100 279/279 [==============================] - 0s 57us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.9556 - val_accuracy: 0.7634 Epoch 97/100 279/279 [==============================] - 0s 61us/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.9558 - val_accuracy: 0.7634 Epoch 98/100 279/279 [==============================] - 0s 57us/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.9560 - val_accuracy: 0.7634 Epoch 99/100 279/279 [==============================] - 0s 64us/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.9567 - val_accuracy: 0.7634 Epoch 00099: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 100/100 279/279 [==============================] - 0s 68us/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.9567 - val_accuracy: 0.7634
In [417]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [418]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
93/93 [==============================] - 0s 43us/step test loss: 0.9567250449170348, test accuracy: 0.7634408473968506
In [419]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7076719576719577
In [420]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.18615751789976132
KMeans¶
In [39]:
X
Out[39]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.709998 | 0.081666 | 2.128489 | 0.497958 | 2.506539 | 1.564462 | -0.544626 | -0.212915 | -0.703695 | 0.450360 | -0.955214 | 0.070111 |
| 1 | 1.306748 | 0.425280 | 1.373850 | -0.510593 | -0.188549 | 0.109582 | 0.202407 | 1.738301 | -0.130093 | 0.581343 | -0.188241 | 0.261952 |
| 2 | 1.734670 | -1.372888 | -1.154236 | 0.358473 | -1.159185 | -1.272362 | -1.103368 | 1.630111 | -1.333754 | -1.332400 | -0.823796 | -1.678793 |
| 3 | 1.734670 | -1.064259 | -0.709050 | 0.536782 | -0.829959 | -1.054348 | -0.888469 | 1.121852 | -0.885262 | -0.799099 | -1.043935 | -1.223713 |
| 4 | 1.734670 | 1.475232 | 0.304731 | 1.153720 | 1.154918 | -0.480466 | 0.141495 | 0.952700 | 0.366988 | -0.320424 | -0.249684 | -0.286209 |
| 5 | -1.357454 | -1.560365 | -0.993389 | 2.575326 | -0.840510 | -0.885550 | 1.098423 | 0.910833 | -1.901272 | -1.828920 | -1.282786 | -1.273477 |
| 6 | 0.321543 | -0.501364 | -0.339054 | 0.091827 | -0.326838 | 0.016241 | -0.078257 | 0.113313 | 1.271259 | -0.267013 | 0.231504 | 1.610523 |
| 7 | 1.097999 | -0.427045 | -0.234103 | 1.592537 | -0.309903 | 2.367725 | -0.109067 | 1.728224 | 0.757555 | 1.272787 | 1.334116 | 1.609735 |
| 8 | 1.734670 | 0.468400 | -0.344386 | -0.090068 | -0.397143 | 1.133967 | -0.709333 | -0.262112 | -0.367753 | 0.321938 | 0.506700 | 2.032213 |
| 9 | -1.359746 | -1.205127 | -0.671317 | -0.932419 | -0.674974 | -1.006055 | -0.652429 | 0.407833 | -0.706174 | 0.460005 | 1.361925 | -0.543693 |
| 10 | 1.587306 | 0.209036 | 1.657840 | 0.476532 | 1.214490 | 1.589653 | 0.700434 | 0.894283 | 0.451061 | 0.995991 | 1.361926 | 1.007772 |
| 11 | 1.734670 | 0.104209 | -0.231131 | -0.245206 | -0.310258 | -0.256497 | -0.023748 | 0.747830 | -0.408037 | -0.453141 | -0.079052 | 1.036713 |
| 12 | 1.035685 | 0.546933 | 0.622772 | -0.701164 | -0.677402 | -1.056329 | -0.597825 | -1.035795 | 1.271259 | 0.922609 | 0.604807 | 1.083653 |
| 13 | 0.972281 | 0.789016 | 0.702993 | 0.179892 | -0.398625 | -0.659406 | -0.324016 | 0.383199 | 1.271259 | 0.519070 | -0.328565 | 1.268306 |
| 14 | -1.133199 | 0.756391 | -0.892669 | -1.306858 | -1.039211 | -0.140379 | -1.019984 | -0.954278 | -0.606554 | -0.698781 | 1.361926 | -0.845614 |
| 15 | -0.286686 | -0.893181 | -0.864608 | -1.025582 | -1.145054 | -1.306954 | -0.907368 | 1.339780 | 0.096326 | 0.053290 | 1.361926 | -0.418235 |
| 16 | -1.078617 | -1.219578 | -0.935650 | -0.892424 | -1.324305 | -0.677583 | -1.147006 | -0.197405 | -1.202819 | -1.032993 | 1.361926 | -0.456323 |
| 17 | -0.779483 | -0.945540 | -0.338284 | -0.640770 | 2.494550 | 1.423590 | 2.049647 | -1.134775 | -0.280816 | -0.841355 | -0.559543 | 2.119588 |
| 18 | -0.539003 | 0.776852 | 0.530745 | -0.519374 | 2.508300 | 1.650614 | 3.324739 | -0.880955 | 0.666814 | -0.537766 | -0.411351 | 1.953562 |
| 19 | -0.654289 | -0.841544 | 0.162215 | -0.248271 | 1.621296 | 1.601864 | 3.324739 | -1.014659 | -0.291794 | -0.730857 | -0.463545 | 2.105096 |
| 20 | 0.213529 | 1.051570 | 2.128489 | 2.214653 | -1.312378 | 2.005231 | -1.146344 | -1.733450 | -1.671687 | -1.708415 | 1.033382 | -1.042990 |
| 21 | -1.008970 | 0.227216 | -0.685589 | -0.789964 | -1.174654 | -0.198868 | -1.080110 | -1.082456 | 0.645207 | -1.226921 | 1.361926 | -0.957975 |
| 22 | -0.509770 | -0.100539 | -0.507952 | -0.571581 | -1.187009 | 0.856048 | -0.968467 | -1.269919 | 1.271259 | -0.525888 | 1.209173 | -0.582980 |
| 23 | -0.434214 | -1.542384 | 0.834388 | -1.429739 | -1.306939 | 1.072244 | -1.147365 | -1.310384 | -2.099689 | 0.794263 | 1.361926 | -1.700448 |
| 24 | 1.024011 | -0.133360 | 2.128488 | -0.497823 | -0.578522 | 0.203487 | 0.200158 | 1.499097 | 0.406494 | 1.107965 | -0.366218 | 0.290605 |
| 25 | 0.996972 | -0.068300 | 0.463479 | -0.462229 | -0.557242 | -0.488353 | 0.319927 | -0.745740 | 0.672891 | 1.272786 | -0.392191 | 0.156034 |
| 26 | 1.734670 | -0.636382 | 0.769493 | -0.963728 | -0.858496 | 0.118250 | -0.164546 | -0.383110 | -0.373161 | 0.540783 | 0.484812 | -0.694416 |
| 27 | 0.939519 | -0.546060 | 1.545261 | -0.614695 | -0.350160 | 0.761016 | -0.827209 | -0.140902 | -1.236174 | 1.272787 | 0.685149 | -0.743315 |
| 28 | 0.065764 | -1.072686 | 1.002347 | 0.205191 | 0.149629 | 0.960761 | -0.441845 | 1.071626 | -0.641837 | 1.004214 | 1.361926 | -0.922358 |
| 29 | 1.734670 | -1.368860 | 0.533780 | -0.644543 | -1.328265 | 0.179244 | -1.144397 | -1.100556 | -2.100911 | -0.305756 | -0.595463 | -1.054706 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 342 | -0.855930 | 0.826496 | -0.453379 | -0.949219 | 0.299850 | -1.090327 | 0.918687 | -1.108240 | -0.838253 | -0.500788 | -0.690370 | 2.119588 |
| 343 | -1.395065 | -0.085953 | 1.922929 | -0.223554 | 1.095776 | -0.911609 | -0.062340 | 1.738301 | -1.726601 | -0.869379 | -1.690911 | 0.185932 |
| 344 | -0.298021 | -1.181741 | -0.986984 | -1.060049 | -0.912388 | -1.297902 | -1.142678 | -1.388649 | -0.921552 | 1.272787 | -1.272055 | -1.334409 |
| 345 | -1.545817 | -1.409239 | -1.442796 | -1.421649 | -0.183976 | -1.246279 | -1.097348 | -1.266514 | -1.348236 | 1.272787 | -1.481572 | -1.042455 |
| 346 | -0.346415 | -1.039454 | -1.018195 | -0.914582 | -0.378597 | -1.221697 | -1.136525 | -0.552667 | -0.552626 | 1.272787 | -1.056006 | -1.074637 |
| 347 | -1.474386 | 0.029941 | -0.538026 | -0.402193 | 2.847023 | 1.102542 | 2.185053 | -1.039952 | -2.054190 | -1.784163 | -1.788691 | -0.164281 |
| 348 | 0.118230 | 0.491129 | -1.420467 | 1.101952 | -1.224983 | 2.813714 | 1.536334 | -1.700918 | 0.266362 | -2.009383 | 0.865783 | -1.676405 |
| 349 | 0.348396 | 0.059205 | -0.665990 | 2.575326 | -0.216770 | 1.953864 | 0.561548 | -1.182548 | 0.195642 | -1.279866 | 0.455845 | -0.516320 |
| 350 | 1.734670 | -0.003146 | 1.568996 | -0.434837 | 0.521805 | 0.570604 | 0.538883 | 1.192315 | -0.104273 | 0.910080 | -0.509533 | 0.299932 |
| 351 | -0.099944 | -0.751191 | 2.128489 | -1.036671 | 0.343684 | 1.099881 | -0.554111 | 0.079251 | -1.057160 | 1.199793 | -1.225953 | -0.635720 |
| 352 | 1.084858 | -0.145339 | 0.813709 | -0.936272 | 0.827416 | 1.989130 | -0.287384 | -0.189383 | -0.826764 | 1.272787 | -0.984655 | -0.043832 |
| 353 | 1.187787 | -0.429263 | -0.071507 | 0.608020 | 0.731972 | 1.256808 | -0.000437 | 0.609195 | 1.271259 | 0.812543 | 0.861217 | 1.341364 |
| 354 | 0.816380 | -0.786929 | -0.447031 | -0.170697 | 0.510269 | 0.818066 | 1.142880 | 0.163693 | 0.937487 | 1.093106 | 1.361926 | 1.303202 |
| 355 | 1.118179 | -0.721368 | -0.381566 | 0.060659 | 0.909982 | 1.044335 | 1.603314 | 0.291249 | 0.641018 | 1.101106 | 1.361926 | 1.616319 |
| 356 | 1.226095 | 0.286296 | -0.087024 | -0.242071 | -0.449252 | -0.446803 | -0.671768 | -0.078598 | 1.271259 | 0.835145 | 0.775448 | 0.595847 |
| 357 | 1.613198 | 0.199015 | 0.291700 | -0.240822 | 0.410003 | 0.405486 | -0.473827 | 0.672002 | 0.944358 | 1.272787 | 0.606300 | 0.775759 |
| 358 | 0.890203 | -0.190496 | 0.624245 | -0.555802 | 0.066592 | -0.255891 | -0.711912 | 0.103160 | 0.368297 | 1.272787 | -0.020119 | 0.448029 |
| 359 | -1.618544 | 2.294335 | -0.896404 | 1.636479 | 0.891626 | -1.405106 | -0.282123 | -1.727463 | 0.411358 | -1.394589 | -1.825966 | 0.156317 |
| 360 | -1.152545 | 1.039906 | -0.408095 | -0.456317 | -0.531367 | -1.073809 | -0.666121 | -1.066545 | 1.271259 | -0.911866 | -1.381364 | -1.175728 |
| 361 | -0.667401 | 2.294335 | 1.170804 | 0.615303 | 1.057413 | -0.853776 | 1.839336 | -1.553629 | 0.257124 | -0.175382 | -1.194146 | -0.159446 |
| 362 | -1.723669 | 0.092346 | -0.153795 | -1.289641 | -0.293218 | -1.396202 | 3.324739 | -0.906893 | -1.978969 | -0.337704 | -1.827015 | 0.302210 |
| 363 | -1.723669 | -0.253829 | 1.824256 | -1.430713 | 0.052559 | -1.405490 | 2.236827 | 0.207454 | -2.097736 | 1.272787 | -1.501135 | 1.113279 |
| 364 | -1.723669 | -0.295522 | 0.733720 | -1.430713 | -0.944463 | -1.405490 | 2.817768 | -1.650910 | -2.100911 | 1.272786 | -1.827015 | -0.821891 |
| 365 | -0.736785 | -0.662679 | 2.128489 | -0.285797 | 0.766661 | 0.905055 | 0.092249 | 0.094889 | -1.072136 | 0.646308 | -0.309608 | -0.624335 |
| 366 | 0.261615 | -0.713681 | 2.128489 | -0.645248 | 0.263270 | 1.334024 | -0.452231 | 1.223459 | -1.251130 | 0.300478 | 0.115550 | -0.729146 |
| 367 | -0.720122 | -0.373744 | 1.948953 | 0.317220 | 1.674285 | 1.748469 | 0.387958 | 0.782162 | -0.402149 | 1.272787 | 0.811932 | -0.459207 |
| 368 | -0.090607 | 0.425445 | 1.059996 | 0.031889 | 0.100842 | 0.641548 | 0.277516 | 1.419713 | 1.271259 | 0.704201 | 0.112480 | 0.417123 |
| 369 | 1.263139 | 0.315568 | 2.128489 | 0.016454 | 1.564635 | 1.785525 | 0.652075 | 1.086427 | 0.193496 | 0.271812 | -0.412142 | 0.549110 |
| 370 | -1.393336 | 0.445276 | -0.245554 | 2.057913 | 1.071731 | -0.067700 | 0.357666 | 1.738300 | 1.036275 | 0.316255 | 0.704801 | -0.474007 |
| 371 | 0.356389 | -0.292506 | -0.400481 | -0.103549 | -0.758012 | 1.042100 | -0.936500 | 1.738300 | 0.568841 | 0.239851 | -0.233628 | 0.122538 |
372 rows × 12 columns
In [40]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[40]:
[4464.0, 3806.9754284522382, 3354.365999848692, 3107.5256199539926, 2946.6963010094414, 2816.3317870211076, 2686.415552171973, 2576.5543366887405, 2504.325344232164, 2391.4415220727014, 2324.241850993652, 2257.160251494757, 2174.641140049278, 2102.896301963741]
In [41]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[41]:
[<matplotlib.lines.Line2D at 0x1ef5e7ceda0>]
K=3
In [42]:
kmeans_ch = KMeans(n_clusters=3, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[42]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [43]:
kmeans_ch.labels_
Out[43]:
array([1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0,
0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 2, 0, 0, 2, 0, 2, 1, 1, 1, 0,
0, 1, 0, 0, 0, 0, 1, 1, 2, 2, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,
1, 0, 0, 2, 2, 2, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 1, 1, 2, 1, 1, 0, 1, 1, 1,
2, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 2, 0, 2, 2, 2, 1, 0, 1,
0, 0, 0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 1, 1, 0, 0, 2,
1, 2, 1, 2, 2, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 0, 1, 2, 2, 2, 1, 1, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1,
2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 0, 0, 0, 1, 1, 2, 2, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1, 1, 1, 2, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 2, 2, 0,
1, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 2, 0, 1, 0,
1, 0, 1, 0, 0, 1, 2, 2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1])
In [44]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[44]:
array([1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0,
0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 2, 0, 0, 2, 0, 2, 1, 1, 1, 0,
0, 1, 0, 0, 0, 0, 1, 1, 2, 2, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,
1, 0, 0, 2, 2, 2, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 1, 1, 2, 1, 1, 0, 1, 1, 1,
2, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 2, 0, 2, 2, 2, 1, 0, 1,
0, 0, 0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0, 2, 0, 2, 1, 1, 0, 0, 2,
1, 2, 1, 2, 2, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 0, 1, 2, 2, 2, 1, 1, 0, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1,
2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 0, 0, 0, 1, 1, 2, 2, 0, 1, 1, 0, 0,
0, 0, 1, 1, 1, 1, 1, 2, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 2, 2, 0,
1, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 0, 0, 1, 2, 2, 0, 2, 0, 1, 0,
1, 0, 1, 0, 0, 1, 2, 2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1])
In [45]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [46]:
X
Out[46]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.709998 | 0.081666 | 2.128489 | 0.497958 | 2.506539 | 1.564462 | -0.544626 | -0.212915 | -0.703695 | 0.450360 | -0.955214 | 0.070111 | 1 | 0 |
| 1 | 1.306748 | 0.425280 | 1.373850 | -0.510593 | -0.188549 | 0.109582 | 0.202407 | 1.738301 | -0.130093 | 0.581343 | -0.188241 | 0.261952 | 1 | 0 |
| 2 | 1.734670 | -1.372888 | -1.154236 | 0.358473 | -1.159185 | -1.272362 | -1.103368 | 1.630111 | -1.333754 | -1.332400 | -0.823796 | -1.678793 | 0 | 0 |
| 3 | 1.734670 | -1.064259 | -0.709050 | 0.536782 | -0.829959 | -1.054348 | -0.888469 | 1.121852 | -0.885262 | -0.799099 | -1.043935 | -1.223713 | 0 | 0 |
| 4 | 1.734670 | 1.475232 | 0.304731 | 1.153720 | 1.154918 | -0.480466 | 0.141495 | 0.952700 | 0.366988 | -0.320424 | -0.249684 | -0.286209 | 1 | 0 |
| 5 | -1.357454 | -1.560365 | -0.993389 | 2.575326 | -0.840510 | -0.885550 | 1.098423 | 0.910833 | -1.901272 | -1.828920 | -1.282786 | -1.273477 | 0 | 0 |
| 6 | 0.321543 | -0.501364 | -0.339054 | 0.091827 | -0.326838 | 0.016241 | -0.078257 | 0.113313 | 1.271259 | -0.267013 | 0.231504 | 1.610523 | 1 | 0 |
| 7 | 1.097999 | -0.427045 | -0.234103 | 1.592537 | -0.309903 | 2.367725 | -0.109067 | 1.728224 | 0.757555 | 1.272787 | 1.334116 | 1.609735 | 1 | 0 |
| 8 | 1.734670 | 0.468400 | -0.344386 | -0.090068 | -0.397143 | 1.133967 | -0.709333 | -0.262112 | -0.367753 | 0.321938 | 0.506700 | 2.032213 | 1 | 0 |
| 9 | -1.359746 | -1.205127 | -0.671317 | -0.932419 | -0.674974 | -1.006055 | -0.652429 | 0.407833 | -0.706174 | 0.460005 | 1.361925 | -0.543693 | 0 | 0 |
| 10 | 1.587306 | 0.209036 | 1.657840 | 0.476532 | 1.214490 | 1.589653 | 0.700434 | 0.894283 | 0.451061 | 0.995991 | 1.361926 | 1.007772 | 1 | 0 |
| 11 | 1.734670 | 0.104209 | -0.231131 | -0.245206 | -0.310258 | -0.256497 | -0.023748 | 0.747830 | -0.408037 | -0.453141 | -0.079052 | 1.036713 | 1 | 0 |
| 12 | 1.035685 | 0.546933 | 0.622772 | -0.701164 | -0.677402 | -1.056329 | -0.597825 | -1.035795 | 1.271259 | 0.922609 | 0.604807 | 1.083653 | 1 | 0 |
| 13 | 0.972281 | 0.789016 | 0.702993 | 0.179892 | -0.398625 | -0.659406 | -0.324016 | 0.383199 | 1.271259 | 0.519070 | -0.328565 | 1.268306 | 1 | 0 |
| 14 | -1.133199 | 0.756391 | -0.892669 | -1.306858 | -1.039211 | -0.140379 | -1.019984 | -0.954278 | -0.606554 | -0.698781 | 1.361926 | -0.845614 | 0 | 0 |
| 15 | -0.286686 | -0.893181 | -0.864608 | -1.025582 | -1.145054 | -1.306954 | -0.907368 | 1.339780 | 0.096326 | 0.053290 | 1.361926 | -0.418235 | 0 | 0 |
| 16 | -1.078617 | -1.219578 | -0.935650 | -0.892424 | -1.324305 | -0.677583 | -1.147006 | -0.197405 | -1.202819 | -1.032993 | 1.361926 | -0.456323 | 0 | 0 |
| 17 | -0.779483 | -0.945540 | -0.338284 | -0.640770 | 2.494550 | 1.423590 | 2.049647 | -1.134775 | -0.280816 | -0.841355 | -0.559543 | 2.119588 | 2 | 0 |
| 18 | -0.539003 | 0.776852 | 0.530745 | -0.519374 | 2.508300 | 1.650614 | 3.324739 | -0.880955 | 0.666814 | -0.537766 | -0.411351 | 1.953562 | 2 | 0 |
| 19 | -0.654289 | -0.841544 | 0.162215 | -0.248271 | 1.621296 | 1.601864 | 3.324739 | -1.014659 | -0.291794 | -0.730857 | -0.463545 | 2.105096 | 2 | 0 |
| 20 | 0.213529 | 1.051570 | 2.128489 | 2.214653 | -1.312378 | 2.005231 | -1.146344 | -1.733450 | -1.671687 | -1.708415 | 1.033382 | -1.042990 | 0 | 0 |
| 21 | -1.008970 | 0.227216 | -0.685589 | -0.789964 | -1.174654 | -0.198868 | -1.080110 | -1.082456 | 0.645207 | -1.226921 | 1.361926 | -0.957975 | 0 | 0 |
| 22 | -0.509770 | -0.100539 | -0.507952 | -0.571581 | -1.187009 | 0.856048 | -0.968467 | -1.269919 | 1.271259 | -0.525888 | 1.209173 | -0.582980 | 0 | 0 |
| 23 | -0.434214 | -1.542384 | 0.834388 | -1.429739 | -1.306939 | 1.072244 | -1.147365 | -1.310384 | -2.099689 | 0.794263 | 1.361926 | -1.700448 | 0 | 0 |
| 24 | 1.024011 | -0.133360 | 2.128488 | -0.497823 | -0.578522 | 0.203487 | 0.200158 | 1.499097 | 0.406494 | 1.107965 | -0.366218 | 0.290605 | 1 | 0 |
| 25 | 0.996972 | -0.068300 | 0.463479 | -0.462229 | -0.557242 | -0.488353 | 0.319927 | -0.745740 | 0.672891 | 1.272786 | -0.392191 | 0.156034 | 1 | 0 |
| 26 | 1.734670 | -0.636382 | 0.769493 | -0.963728 | -0.858496 | 0.118250 | -0.164546 | -0.383110 | -0.373161 | 0.540783 | 0.484812 | -0.694416 | 0 | 0 |
| 27 | 0.939519 | -0.546060 | 1.545261 | -0.614695 | -0.350160 | 0.761016 | -0.827209 | -0.140902 | -1.236174 | 1.272787 | 0.685149 | -0.743315 | 1 | 0 |
| 28 | 0.065764 | -1.072686 | 1.002347 | 0.205191 | 0.149629 | 0.960761 | -0.441845 | 1.071626 | -0.641837 | 1.004214 | 1.361926 | -0.922358 | 1 | 0 |
| 29 | 1.734670 | -1.368860 | 0.533780 | -0.644543 | -1.328265 | 0.179244 | -1.144397 | -1.100556 | -2.100911 | -0.305756 | -0.595463 | -1.054706 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 342 | -0.855930 | 0.826496 | -0.453379 | -0.949219 | 0.299850 | -1.090327 | 0.918687 | -1.108240 | -0.838253 | -0.500788 | -0.690370 | 2.119588 | 2 | 1 |
| 343 | -1.395065 | -0.085953 | 1.922929 | -0.223554 | 1.095776 | -0.911609 | -0.062340 | 1.738301 | -1.726601 | -0.869379 | -1.690911 | 0.185932 | 2 | 1 |
| 344 | -0.298021 | -1.181741 | -0.986984 | -1.060049 | -0.912388 | -1.297902 | -1.142678 | -1.388649 | -0.921552 | 1.272787 | -1.272055 | -1.334409 | 0 | 1 |
| 345 | -1.545817 | -1.409239 | -1.442796 | -1.421649 | -0.183976 | -1.246279 | -1.097348 | -1.266514 | -1.348236 | 1.272787 | -1.481572 | -1.042455 | 0 | 1 |
| 346 | -0.346415 | -1.039454 | -1.018195 | -0.914582 | -0.378597 | -1.221697 | -1.136525 | -0.552667 | -0.552626 | 1.272787 | -1.056006 | -1.074637 | 0 | 1 |
| 347 | -1.474386 | 0.029941 | -0.538026 | -0.402193 | 2.847023 | 1.102542 | 2.185053 | -1.039952 | -2.054190 | -1.784163 | -1.788691 | -0.164281 | 2 | 1 |
| 348 | 0.118230 | 0.491129 | -1.420467 | 1.101952 | -1.224983 | 2.813714 | 1.536334 | -1.700918 | 0.266362 | -2.009383 | 0.865783 | -1.676405 | 0 | 1 |
| 349 | 0.348396 | 0.059205 | -0.665990 | 2.575326 | -0.216770 | 1.953864 | 0.561548 | -1.182548 | 0.195642 | -1.279866 | 0.455845 | -0.516320 | 1 | 1 |
| 350 | 1.734670 | -0.003146 | 1.568996 | -0.434837 | 0.521805 | 0.570604 | 0.538883 | 1.192315 | -0.104273 | 0.910080 | -0.509533 | 0.299932 | 1 | 1 |
| 351 | -0.099944 | -0.751191 | 2.128489 | -1.036671 | 0.343684 | 1.099881 | -0.554111 | 0.079251 | -1.057160 | 1.199793 | -1.225953 | -0.635720 | 1 | 1 |
| 352 | 1.084858 | -0.145339 | 0.813709 | -0.936272 | 0.827416 | 1.989130 | -0.287384 | -0.189383 | -0.826764 | 1.272787 | -0.984655 | -0.043832 | 1 | 1 |
| 353 | 1.187787 | -0.429263 | -0.071507 | 0.608020 | 0.731972 | 1.256808 | -0.000437 | 0.609195 | 1.271259 | 0.812543 | 0.861217 | 1.341364 | 1 | 1 |
| 354 | 0.816380 | -0.786929 | -0.447031 | -0.170697 | 0.510269 | 0.818066 | 1.142880 | 0.163693 | 0.937487 | 1.093106 | 1.361926 | 1.303202 | 1 | 1 |
| 355 | 1.118179 | -0.721368 | -0.381566 | 0.060659 | 0.909982 | 1.044335 | 1.603314 | 0.291249 | 0.641018 | 1.101106 | 1.361926 | 1.616319 | 1 | 1 |
| 356 | 1.226095 | 0.286296 | -0.087024 | -0.242071 | -0.449252 | -0.446803 | -0.671768 | -0.078598 | 1.271259 | 0.835145 | 0.775448 | 0.595847 | 1 | 1 |
| 357 | 1.613198 | 0.199015 | 0.291700 | -0.240822 | 0.410003 | 0.405486 | -0.473827 | 0.672002 | 0.944358 | 1.272787 | 0.606300 | 0.775759 | 1 | 1 |
| 358 | 0.890203 | -0.190496 | 0.624245 | -0.555802 | 0.066592 | -0.255891 | -0.711912 | 0.103160 | 0.368297 | 1.272787 | -0.020119 | 0.448029 | 1 | 1 |
| 359 | -1.618544 | 2.294335 | -0.896404 | 1.636479 | 0.891626 | -1.405106 | -0.282123 | -1.727463 | 0.411358 | -1.394589 | -1.825966 | 0.156317 | 2 | 1 |
| 360 | -1.152545 | 1.039906 | -0.408095 | -0.456317 | -0.531367 | -1.073809 | -0.666121 | -1.066545 | 1.271259 | -0.911866 | -1.381364 | -1.175728 | 0 | 1 |
| 361 | -0.667401 | 2.294335 | 1.170804 | 0.615303 | 1.057413 | -0.853776 | 1.839336 | -1.553629 | 0.257124 | -0.175382 | -1.194146 | -0.159446 | 2 | 1 |
| 362 | -1.723669 | 0.092346 | -0.153795 | -1.289641 | -0.293218 | -1.396202 | 3.324739 | -0.906893 | -1.978969 | -0.337704 | -1.827015 | 0.302210 | 2 | 1 |
| 363 | -1.723669 | -0.253829 | 1.824256 | -1.430713 | 0.052559 | -1.405490 | 2.236827 | 0.207454 | -2.097736 | 1.272787 | -1.501135 | 1.113279 | 2 | 1 |
| 364 | -1.723669 | -0.295522 | 0.733720 | -1.430713 | -0.944463 | -1.405490 | 2.817768 | -1.650910 | -2.100911 | 1.272786 | -1.827015 | -0.821891 | 2 | 1 |
| 365 | -0.736785 | -0.662679 | 2.128489 | -0.285797 | 0.766661 | 0.905055 | 0.092249 | 0.094889 | -1.072136 | 0.646308 | -0.309608 | -0.624335 | 1 | 1 |
| 366 | 0.261615 | -0.713681 | 2.128489 | -0.645248 | 0.263270 | 1.334024 | -0.452231 | 1.223459 | -1.251130 | 0.300478 | 0.115550 | -0.729146 | 1 | 1 |
| 367 | -0.720122 | -0.373744 | 1.948953 | 0.317220 | 1.674285 | 1.748469 | 0.387958 | 0.782162 | -0.402149 | 1.272787 | 0.811932 | -0.459207 | 1 | 1 |
| 368 | -0.090607 | 0.425445 | 1.059996 | 0.031889 | 0.100842 | 0.641548 | 0.277516 | 1.419713 | 1.271259 | 0.704201 | 0.112480 | 0.417123 | 1 | 1 |
| 369 | 1.263139 | 0.315568 | 2.128489 | 0.016454 | 1.564635 | 1.785525 | 0.652075 | 1.086427 | 0.193496 | 0.271812 | -0.412142 | 0.549110 | 1 | 1 |
| 370 | -1.393336 | 0.445276 | -0.245554 | 2.057913 | 1.071731 | -0.067700 | 0.357666 | 1.738300 | 1.036275 | 0.316255 | 0.704801 | -0.474007 | 1 | 1 |
| 371 | 0.356389 | -0.292506 | -0.400481 | -0.103549 | -0.758012 | 1.042100 | -0.936500 | 1.738300 | 0.568841 | 0.239851 | -0.233628 | 0.122538 | 1 | 1 |
372 rows × 14 columns
In [47]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[47]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ef5e801358>
In [48]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[1]))
Club De Banqueros y Empresarios¶
ANN¶
In [421]:
X = df_n_ps_std_ch[1]
In [422]:
y = df_n_ps[1]['chosen']
In [423]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [424]:
X_train.shape
Out[424]:
(191, 12)
In [53]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [54]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [55]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [60]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.004, 'max_iter': 500}, que permiten obtener un Accuracy de 78.53% y un Kappa del 40.11
Tiempo total: 24.99 minutos
In [425]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.004
epochs = 500
In [426]:
input_tensor = Input(shape = (n0,))
In [427]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [428]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [429]:
model.summary()
Model: "model_22" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_22 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_70 (Dense) (None, 10) 130 _________________________________________________________________ dense_71 (Dense) (None, 1) 11 ================================================================= Total params: 141 Trainable params: 141 Non-trainable params: 0 _________________________________________________________________
In [430]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 191 samples, validate on 64 samples Epoch 1/500 191/191 [==============================] - 0s 1ms/step - loss: 0.8012 - accuracy: 0.4607 - val_loss: 0.7811 - val_accuracy: 0.4688 Epoch 2/500 191/191 [==============================] - 0s 63us/step - loss: 0.7341 - accuracy: 0.4921 - val_loss: 0.7282 - val_accuracy: 0.5156 Epoch 3/500 191/191 [==============================] - 0s 68us/step - loss: 0.6797 - accuracy: 0.5602 - val_loss: 0.6882 - val_accuracy: 0.5469 Epoch 4/500 191/191 [==============================] - 0s 99us/step - loss: 0.6390 - accuracy: 0.6283 - val_loss: 0.6650 - val_accuracy: 0.5469 Epoch 5/500 191/191 [==============================] - 0s 73us/step - loss: 0.6126 - accuracy: 0.6754 - val_loss: 0.6514 - val_accuracy: 0.6250 Epoch 6/500 191/191 [==============================] - 0s 63us/step - loss: 0.5926 - accuracy: 0.6963 - val_loss: 0.6429 - val_accuracy: 0.6250 Epoch 7/500 191/191 [==============================] - 0s 63us/step - loss: 0.5764 - accuracy: 0.7120 - val_loss: 0.6381 - val_accuracy: 0.6406 Epoch 8/500 191/191 [==============================] - 0s 63us/step - loss: 0.5633 - accuracy: 0.7277 - val_loss: 0.6353 - val_accuracy: 0.6406 Epoch 9/500 191/191 [==============================] - 0s 58us/step - loss: 0.5510 - accuracy: 0.7435 - val_loss: 0.6376 - val_accuracy: 0.6562 Epoch 10/500 191/191 [==============================] - 0s 58us/step - loss: 0.5416 - accuracy: 0.7435 - val_loss: 0.6412 - val_accuracy: 0.6406 Epoch 11/500 191/191 [==============================] - 0s 73us/step - loss: 0.5322 - accuracy: 0.7382 - val_loss: 0.6441 - val_accuracy: 0.6250 Epoch 12/500 191/191 [==============================] - 0s 63us/step - loss: 0.5248 - accuracy: 0.7382 - val_loss: 0.6483 - val_accuracy: 0.6562 Epoch 13/500 191/191 [==============================] - 0s 68us/step - loss: 0.5179 - accuracy: 0.7330 - val_loss: 0.6519 - val_accuracy: 0.6562 Epoch 14/500 191/191 [==============================] - 0s 68us/step - loss: 0.5124 - accuracy: 0.7330 - val_loss: 0.6549 - val_accuracy: 0.6562 Epoch 15/500 191/191 [==============================] - 0s 73us/step - loss: 0.5067 - accuracy: 0.7382 - val_loss: 0.6590 - val_accuracy: 0.6562 Epoch 16/500 191/191 [==============================] - 0s 73us/step - loss: 0.5023 - accuracy: 0.7382 - val_loss: 0.6634 - val_accuracy: 0.6719 Epoch 17/500 191/191 [==============================] - 0s 68us/step - loss: 0.4988 - accuracy: 0.7382 - val_loss: 0.6681 - val_accuracy: 0.6719 Epoch 18/500 191/191 [==============================] - 0s 68us/step - loss: 0.4943 - accuracy: 0.7435 - val_loss: 0.6703 - val_accuracy: 0.6719 Epoch 19/500 191/191 [==============================] - 0s 68us/step - loss: 0.4903 - accuracy: 0.7539 - val_loss: 0.6718 - val_accuracy: 0.6719 Epoch 20/500 191/191 [==============================] - 0s 68us/step - loss: 0.4874 - accuracy: 0.7539 - val_loss: 0.6743 - val_accuracy: 0.6719 Epoch 21/500 191/191 [==============================] - 0s 68us/step - loss: 0.4848 - accuracy: 0.7539 - val_loss: 0.6766 - val_accuracy: 0.6719 Epoch 22/500 191/191 [==============================] - 0s 63us/step - loss: 0.4821 - accuracy: 0.7539 - val_loss: 0.6816 - val_accuracy: 0.6719 Epoch 23/500 191/191 [==============================] - 0s 58us/step - loss: 0.4789 - accuracy: 0.7539 - val_loss: 0.6825 - val_accuracy: 0.6719 Epoch 24/500 191/191 [==============================] - 0s 68us/step - loss: 0.4769 - accuracy: 0.7539 - val_loss: 0.6855 - val_accuracy: 0.6562 Epoch 25/500 191/191 [==============================] - 0s 63us/step - loss: 0.4745 - accuracy: 0.7539 - val_loss: 0.6863 - val_accuracy: 0.6562 Epoch 26/500 191/191 [==============================] - 0s 89us/step - loss: 0.4714 - accuracy: 0.7592 - val_loss: 0.6885 - val_accuracy: 0.6562 Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 27/500 191/191 [==============================] - 0s 89us/step - loss: 0.4696 - accuracy: 0.7592 - val_loss: 0.6896 - val_accuracy: 0.6562 Epoch 28/500 191/191 [==============================] - 0s 78us/step - loss: 0.4682 - accuracy: 0.7696 - val_loss: 0.6888 - val_accuracy: 0.6562 Epoch 29/500 191/191 [==============================] - 0s 73us/step - loss: 0.4671 - accuracy: 0.7644 - val_loss: 0.6874 - val_accuracy: 0.6562 Epoch 30/500 191/191 [==============================] - 0s 68us/step - loss: 0.4663 - accuracy: 0.7749 - val_loss: 0.6893 - val_accuracy: 0.6562 Epoch 31/500 191/191 [==============================] - 0s 68us/step - loss: 0.4650 - accuracy: 0.7749 - val_loss: 0.6893 - val_accuracy: 0.6562 Epoch 32/500 191/191 [==============================] - 0s 68us/step - loss: 0.4640 - accuracy: 0.7801 - val_loss: 0.6907 - val_accuracy: 0.6562 Epoch 33/500 191/191 [==============================] - 0s 63us/step - loss: 0.4626 - accuracy: 0.7906 - val_loss: 0.6901 - val_accuracy: 0.6562 Epoch 34/500 191/191 [==============================] - 0s 63us/step - loss: 0.4617 - accuracy: 0.7853 - val_loss: 0.6901 - val_accuracy: 0.6562 Epoch 35/500 191/191 [==============================] - 0s 63us/step - loss: 0.4605 - accuracy: 0.7958 - val_loss: 0.6905 - val_accuracy: 0.6562 Epoch 36/500 191/191 [==============================] - 0s 63us/step - loss: 0.4599 - accuracy: 0.7906 - val_loss: 0.6900 - val_accuracy: 0.6562 Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 37/500 191/191 [==============================] - 0s 63us/step - loss: 0.4582 - accuracy: 0.7906 - val_loss: 0.6902 - val_accuracy: 0.6562 Epoch 38/500 191/191 [==============================] - 0s 63us/step - loss: 0.4579 - accuracy: 0.7906 - val_loss: 0.6908 - val_accuracy: 0.6562 Epoch 39/500 191/191 [==============================] - 0s 63us/step - loss: 0.4572 - accuracy: 0.7906 - val_loss: 0.6913 - val_accuracy: 0.6562 Epoch 40/500 191/191 [==============================] - 0s 73us/step - loss: 0.4568 - accuracy: 0.7958 - val_loss: 0.6919 - val_accuracy: 0.6562 Epoch 41/500 191/191 [==============================] - 0s 68us/step - loss: 0.4564 - accuracy: 0.7958 - val_loss: 0.6923 - val_accuracy: 0.6562 Epoch 42/500 191/191 [==============================] - 0s 68us/step - loss: 0.4556 - accuracy: 0.7958 - val_loss: 0.6925 - val_accuracy: 0.6562 Epoch 43/500 191/191 [==============================] - 0s 73us/step - loss: 0.4551 - accuracy: 0.7958 - val_loss: 0.6927 - val_accuracy: 0.6562 Epoch 44/500 191/191 [==============================] - 0s 73us/step - loss: 0.4546 - accuracy: 0.7958 - val_loss: 0.6930 - val_accuracy: 0.6562 Epoch 45/500 191/191 [==============================] - 0s 73us/step - loss: 0.4540 - accuracy: 0.7958 - val_loss: 0.6926 - val_accuracy: 0.6562 Epoch 46/500 191/191 [==============================] - 0s 63us/step - loss: 0.4535 - accuracy: 0.7958 - val_loss: 0.6925 - val_accuracy: 0.6562 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 47/500 191/191 [==============================] - 0s 68us/step - loss: 0.4528 - accuracy: 0.7958 - val_loss: 0.6927 - val_accuracy: 0.6562 Epoch 48/500 191/191 [==============================] - 0s 73us/step - loss: 0.4526 - accuracy: 0.7958 - val_loss: 0.6928 - val_accuracy: 0.6562 Epoch 49/500 191/191 [==============================] - 0s 73us/step - loss: 0.4523 - accuracy: 0.7958 - val_loss: 0.6930 - val_accuracy: 0.6562 Epoch 50/500 191/191 [==============================] - 0s 68us/step - loss: 0.4520 - accuracy: 0.7958 - val_loss: 0.6930 - val_accuracy: 0.6562 Epoch 51/500 191/191 [==============================] - 0s 68us/step - loss: 0.4518 - accuracy: 0.7958 - val_loss: 0.6933 - val_accuracy: 0.6562 Epoch 52/500 191/191 [==============================] - 0s 68us/step - loss: 0.4515 - accuracy: 0.7958 - val_loss: 0.6932 - val_accuracy: 0.6562 Epoch 53/500 191/191 [==============================] - 0s 52us/step - loss: 0.4513 - accuracy: 0.7958 - val_loss: 0.6932 - val_accuracy: 0.6562 Epoch 54/500 191/191 [==============================] - 0s 58us/step - loss: 0.4510 - accuracy: 0.7958 - val_loss: 0.6932 - val_accuracy: 0.6562 Epoch 55/500 191/191 [==============================] - 0s 105us/step - loss: 0.4507 - accuracy: 0.7958 - val_loss: 0.6931 - val_accuracy: 0.6562 Epoch 56/500 191/191 [==============================] - 0s 84us/step - loss: 0.4504 - accuracy: 0.7958 - val_loss: 0.6932 - val_accuracy: 0.6562 Epoch 00056: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 57/500 191/191 [==============================] - 0s 84us/step - loss: 0.4501 - accuracy: 0.7958 - val_loss: 0.6933 - val_accuracy: 0.6562 Epoch 58/500 191/191 [==============================] - 0s 78us/step - loss: 0.4500 - accuracy: 0.7958 - val_loss: 0.6934 - val_accuracy: 0.6562 Epoch 59/500 191/191 [==============================] - 0s 52us/step - loss: 0.4498 - accuracy: 0.7958 - val_loss: 0.6934 - val_accuracy: 0.6562 Epoch 60/500 191/191 [==============================] - 0s 63us/step - loss: 0.4497 - accuracy: 0.7958 - val_loss: 0.6933 - val_accuracy: 0.6562 Epoch 61/500 191/191 [==============================] - 0s 58us/step - loss: 0.4496 - accuracy: 0.7958 - val_loss: 0.6933 - val_accuracy: 0.6562 Epoch 62/500 191/191 [==============================] - 0s 58us/step - loss: 0.4494 - accuracy: 0.7958 - val_loss: 0.6935 - val_accuracy: 0.6562 Epoch 63/500 191/191 [==============================] - 0s 58us/step - loss: 0.4493 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 64/500 191/191 [==============================] - 0s 63us/step - loss: 0.4492 - accuracy: 0.7958 - val_loss: 0.6934 - val_accuracy: 0.6562 Epoch 65/500 191/191 [==============================] - 0s 58us/step - loss: 0.4490 - accuracy: 0.7958 - val_loss: 0.6935 - val_accuracy: 0.6562 Epoch 66/500 191/191 [==============================] - 0s 58us/step - loss: 0.4489 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 00066: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 67/500 191/191 [==============================] - 0s 58us/step - loss: 0.4487 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 68/500 191/191 [==============================] - 0s 73us/step - loss: 0.4487 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 69/500 191/191 [==============================] - 0s 63us/step - loss: 0.4486 - accuracy: 0.7958 - val_loss: 0.6935 - val_accuracy: 0.6562 Epoch 70/500 191/191 [==============================] - 0s 52us/step - loss: 0.4486 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 71/500 191/191 [==============================] - 0s 52us/step - loss: 0.4484 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 72/500 191/191 [==============================] - 0s 52us/step - loss: 0.4484 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 73/500 191/191 [==============================] - 0s 58us/step - loss: 0.4483 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 74/500 191/191 [==============================] - 0s 52us/step - loss: 0.4482 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 75/500 191/191 [==============================] - 0s 58us/step - loss: 0.4482 - accuracy: 0.7958 - val_loss: 0.6936 - val_accuracy: 0.6562 Epoch 76/500 191/191 [==============================] - 0s 58us/step - loss: 0.4481 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 00076: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 77/500 191/191 [==============================] - 0s 58us/step - loss: 0.4480 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 78/500 191/191 [==============================] - 0s 47us/step - loss: 0.4480 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 79/500 191/191 [==============================] - 0s 58us/step - loss: 0.4480 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 80/500 191/191 [==============================] - 0s 110us/step - loss: 0.4479 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 81/500 191/191 [==============================] - 0s 63us/step - loss: 0.4479 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 82/500 191/191 [==============================] - 0s 68us/step - loss: 0.4479 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 83/500 191/191 [==============================] - 0s 58us/step - loss: 0.4478 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 84/500 191/191 [==============================] - 0s 58us/step - loss: 0.4478 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 85/500 191/191 [==============================] - 0s 52us/step - loss: 0.4477 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 86/500 191/191 [==============================] - 0s 47us/step - loss: 0.4477 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00086: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 87/500 191/191 [==============================] - 0s 52us/step - loss: 0.4477 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 88/500 191/191 [==============================] - 0s 58us/step - loss: 0.4477 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 89/500 191/191 [==============================] - 0s 52us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 90/500 191/191 [==============================] - 0s 58us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6937 - val_accuracy: 0.6562 Epoch 91/500 191/191 [==============================] - 0s 68us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 92/500 191/191 [==============================] - 0s 52us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 93/500 191/191 [==============================] - 0s 52us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 94/500 191/191 [==============================] - 0s 63us/step - loss: 0.4476 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 95/500 191/191 [==============================] - 0s 58us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 96/500 191/191 [==============================] - 0s 58us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00096: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 97/500 191/191 [==============================] - 0s 58us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 98/500 191/191 [==============================] - 0s 68us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 99/500 191/191 [==============================] - 0s 68us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 100/500 191/191 [==============================] - 0s 63us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 101/500 191/191 [==============================] - 0s 52us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 102/500 191/191 [==============================] - 0s 58us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 103/500 191/191 [==============================] - 0s 52us/step - loss: 0.4475 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 104/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 105/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 106/500 191/191 [==============================] - 0s 120us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00106: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 107/500 191/191 [==============================] - 0s 63us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 108/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 109/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 110/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 111/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 112/500 191/191 [==============================] - 0s 63us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 113/500 191/191 [==============================] - 0s 63us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 114/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 115/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 116/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00116: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 117/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 118/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 119/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 120/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 121/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 122/500 191/191 [==============================] - 0s 58us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 123/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 124/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 125/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 126/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00126: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 127/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 128/500 191/191 [==============================] - 0s 52us/step - loss: 0.4474 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 129/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 130/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 131/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 132/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 133/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 134/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 135/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 136/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00136: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 137/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 138/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 139/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 140/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 141/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 142/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 143/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 144/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 145/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 146/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00146: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 147/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 148/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 149/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 150/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 151/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 152/500 191/191 [==============================] - ETA: 0s - loss: 0.5667 - accuracy: 0.68 - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 153/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 154/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 155/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 156/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00156: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 157/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 158/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 159/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 160/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 161/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 162/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 163/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 164/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 165/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 166/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00166: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 167/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 168/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 169/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 170/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 171/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 172/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 173/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 174/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 175/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 176/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00176: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 177/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 178/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 179/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 180/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 181/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 182/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 183/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 184/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 185/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 186/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00186: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 187/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 188/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 189/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 190/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 191/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 192/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 193/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 194/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 195/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 196/500 191/191 [==============================] - 0s 99us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00196: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 197/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 198/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 199/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 200/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 201/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 202/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 203/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 204/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 205/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 206/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00206: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 207/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 208/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 209/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 210/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 211/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 212/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 213/500 191/191 [==============================] - 0s 89us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 214/500 191/191 [==============================] - 0s 94us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 215/500 191/191 [==============================] - 0s 94us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 216/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00216: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 217/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 218/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 219/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 220/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 221/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 222/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 223/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 224/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 225/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 226/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00226: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 227/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 228/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 229/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 230/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 231/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 232/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 233/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 234/500 191/191 [==============================] - 0s 99us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 235/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 236/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00236: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 237/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 238/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 239/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 240/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 241/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 242/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 243/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 244/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 245/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 246/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00246: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 247/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 248/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 249/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 250/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 251/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 252/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 253/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 254/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 255/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 256/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00256: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 257/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 258/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 259/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 260/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 261/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 262/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 263/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 264/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 265/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 266/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00266: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 267/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 268/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 269/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 270/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 271/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 272/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 273/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 274/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 275/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 276/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00276: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 277/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 278/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 279/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 280/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 281/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 282/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 283/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 284/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 285/500 191/191 [==============================] - 0s 94us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 286/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00286: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 287/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 288/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 289/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 290/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 291/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 292/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 293/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 294/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 295/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 296/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00296: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 297/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 298/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 299/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 300/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 301/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 302/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 303/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 304/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 305/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 306/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00306: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 307/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 308/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 309/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 310/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 311/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 312/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 313/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 314/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 315/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 316/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00316: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 317/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 318/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 319/500 191/191 [==============================] - 0s 105us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 320/500 191/191 [==============================] - 0s 89us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 321/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 322/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 323/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 324/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 325/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 326/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00326: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 327/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 328/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 329/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 330/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 331/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 332/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 333/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 334/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 335/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 336/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00336: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 337/500 191/191 [==============================] - 0s 42us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 338/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 339/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 340/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 341/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 342/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 343/500 191/191 [==============================] - 0s 42us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 344/500 191/191 [==============================] - 0s 42us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 345/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 346/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00346: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 347/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 348/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 349/500 191/191 [==============================] - 0s 42us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 350/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 351/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 352/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 353/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 354/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 355/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 356/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00356: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 357/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 358/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 359/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 360/500 191/191 [==============================] - 0s 105us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 361/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 362/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 363/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 364/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 365/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 366/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00366: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 367/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 368/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 369/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 370/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 371/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 372/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 373/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 374/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 375/500 191/191 [==============================] - 0s 84us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 376/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00376: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 377/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 378/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 379/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 380/500 191/191 [==============================] - 0s 110us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 381/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 382/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 383/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 384/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 385/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 386/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00386: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 387/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 388/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 389/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 390/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 391/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 392/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 393/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 394/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 395/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 396/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00396: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 397/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 398/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 399/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 400/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 401/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 402/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 403/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 404/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 405/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 406/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00406: ReduceLROnPlateau reducing learning rate to 7.275957959772868e-15. Epoch 407/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 408/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 409/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 410/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 411/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 412/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 413/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 414/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 415/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 416/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00416: ReduceLROnPlateau reducing learning rate to 3.637978979886434e-15. Epoch 417/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 418/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 419/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 420/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 421/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 422/500 191/191 [==============================] - 0s 99us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 423/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 424/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 425/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 426/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00426: ReduceLROnPlateau reducing learning rate to 1.818989489943217e-15. Epoch 427/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 428/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 429/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 430/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 431/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 432/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 433/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 434/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 435/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 436/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00436: ReduceLROnPlateau reducing learning rate to 9.094947449716085e-16. Epoch 437/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 438/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 439/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 440/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 441/500 191/191 [==============================] - 0s 115us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 442/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 443/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 444/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 445/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 446/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00446: ReduceLROnPlateau reducing learning rate to 4.547473724858043e-16. Epoch 447/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 448/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 449/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 450/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 451/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 452/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 453/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 454/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 455/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 456/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00456: ReduceLROnPlateau reducing learning rate to 2.2737368624290214e-16. Epoch 457/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 458/500 191/191 [==============================] - 0s 73us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 459/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 460/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 461/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 462/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 463/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 464/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 465/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 466/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00466: ReduceLROnPlateau reducing learning rate to 1.1368684312145107e-16. Epoch 467/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 468/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 469/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 470/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 471/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 472/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 473/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 474/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 475/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 476/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00476: ReduceLROnPlateau reducing learning rate to 5.684342156072553e-17. Epoch 477/500 191/191 [==============================] - 0s 68us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 478/500 191/191 [==============================] - 0s 78us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 479/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 480/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 481/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 482/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 483/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 484/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 485/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 486/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00486: ReduceLROnPlateau reducing learning rate to 2.842171078036277e-17. Epoch 487/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 488/500 191/191 [==============================] - 0s 47us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 489/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 490/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 491/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 492/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 493/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 494/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 495/500 191/191 [==============================] - 0s 99us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 496/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 00496: ReduceLROnPlateau reducing learning rate to 1.4210855390181384e-17. Epoch 497/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 498/500 191/191 [==============================] - 0s 63us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 499/500 191/191 [==============================] - 0s 58us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562 Epoch 500/500 191/191 [==============================] - 0s 52us/step - loss: 0.4473 - accuracy: 0.7958 - val_loss: 0.6938 - val_accuracy: 0.6562
In [431]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 500)
In [432]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
64/64 [==============================] - 0s 47us/step test loss: 0.6938145160675049, test accuracy: 0.65625
In [433]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.5519480519480519
In [434]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.053763440860215006
KMeans¶
In [71]:
X
Out[71]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.369691 | -0.881824 | -0.095656 | -0.923999 | -0.030645 | -0.834931 | -1.031650 | -0.840942 | -0.677716 | 1.084098 | -1.064999 | -1.156623 |
| 1 | -0.175875 | -0.403800 | -0.657709 | -0.201259 | 1.691433 | -0.672783 | -0.119944 | -0.440080 | 0.339906 | 1.084098 | 0.504608 | 0.931676 |
| 2 | 0.894452 | -0.189794 | 1.959063 | 0.169276 | -0.403611 | -1.036954 | 1.447615 | -0.340767 | -0.846170 | -0.515065 | -0.699878 | 0.032355 |
| 3 | 0.060782 | -0.392075 | 0.826233 | -0.048480 | 1.789786 | -0.552163 | 0.121028 | -0.111355 | 0.220614 | 1.084098 | 0.073241 | 1.176257 |
| 4 | -1.116536 | -0.923742 | -1.238971 | -0.919598 | 0.313068 | -1.160111 | 1.316032 | -0.700013 | -1.600210 | 1.084098 | -1.072155 | 1.270095 |
| 5 | -1.082752 | -0.067472 | -1.142511 | -0.923999 | 0.389201 | -1.061180 | 2.067035 | -0.269819 | -1.620482 | -0.468180 | -0.962315 | 1.853511 |
| 6 | -1.202528 | -0.776453 | -1.135820 | -0.773268 | -0.409932 | -0.832224 | -0.631931 | -0.119492 | 1.620288 | 0.162930 | -1.063358 | 0.001987 |
| 7 | -1.206944 | 0.500703 | -0.243295 | -0.611928 | 0.262760 | -0.855004 | 0.001441 | -1.579225 | 0.245997 | 1.084098 | -1.197067 | -0.681282 |
| 8 | -0.982006 | 0.421831 | 0.984997 | -0.550391 | 0.215104 | -1.100712 | 2.248321 | -0.336428 | 1.282109 | 1.084098 | -0.061910 | 0.480931 |
| 9 | 1.703175 | 1.029234 | 1.508859 | 0.964653 | 2.164165 | 0.482073 | -0.794175 | 0.729102 | 0.721867 | 0.439251 | 0.198086 | 1.778147 |
| 10 | 1.528139 | 0.898498 | 0.923889 | 0.634045 | 2.016059 | 0.674138 | -0.430188 | 0.558129 | 1.200855 | 0.618822 | 0.291110 | 1.853511 |
| 11 | 0.334361 | -0.301383 | -0.450307 | -0.470199 | -0.977542 | 0.863046 | -0.396657 | -0.882307 | 0.259614 | 0.809320 | 1.750633 | 0.245556 |
| 12 | 0.597458 | 0.773201 | 0.182265 | 0.104921 | 0.580017 | 0.644184 | 1.433111 | 1.735353 | -0.712181 | -0.818426 | -0.129551 | -0.039236 |
| 13 | -0.493625 | 1.341798 | -0.632970 | 2.666081 | 2.015672 | 1.537362 | 2.432631 | 0.500840 | 1.337627 | 1.084098 | -0.069418 | 1.097765 |
| 14 | 0.962230 | 0.028408 | 0.059003 | -0.233385 | 1.425585 | 1.226062 | 1.160066 | 1.604723 | 0.272753 | 1.084098 | 0.709073 | 1.356660 |
| 15 | -0.843247 | 0.160055 | 1.959063 | -0.354971 | -0.294051 | -0.485118 | -0.796417 | -0.212355 | -0.168152 | -0.782723 | -0.232169 | -0.234956 |
| 16 | -0.256403 | 1.322075 | 1.520118 | 0.907583 | 1.032003 | 0.577931 | 0.414295 | 0.551597 | 0.783033 | 1.084098 | 0.088744 | 1.848078 |
| 17 | -0.456352 | 2.142046 | 1.959063 | 1.734877 | 1.472675 | 0.985568 | 0.646614 | 0.230853 | 0.051480 | -1.122573 | -0.594948 | 0.587848 |
| 18 | -0.637040 | -1.030219 | -1.165495 | -0.606800 | 2.164165 | -1.161299 | -0.882343 | 1.727762 | -1.021803 | -1.420384 | -1.197067 | -1.157909 |
| 19 | -1.204564 | -1.022455 | -0.683305 | -0.923999 | -0.935167 | -0.988596 | -0.688535 | 1.735353 | -0.372860 | -1.581696 | -1.197067 | -0.315002 |
| 20 | -1.077420 | -0.314272 | 1.036699 | -0.850174 | -0.071202 | -1.104286 | 0.102157 | -0.867378 | -0.464391 | 0.591092 | -1.048902 | 1.853510 |
| 21 | -0.494728 | -1.023105 | -0.383945 | -0.918858 | 0.498899 | -1.042513 | -0.072226 | -0.187025 | -0.589871 | 1.084098 | -0.986967 | 0.537859 |
| 22 | -0.525116 | 0.187277 | 1.220635 | -0.221678 | 0.589822 | -1.115053 | 0.421737 | 0.656325 | -0.098846 | 1.084098 | -0.740109 | 1.853156 |
| 23 | -0.282675 | 0.571926 | -0.333097 | 2.860439 | 1.250860 | 1.434107 | 2.006573 | 0.362041 | 1.620288 | 0.896295 | -0.274184 | 1.257508 |
| 24 | 1.898264 | 1.240876 | 0.302806 | -0.427292 | -0.709276 | -0.159183 | -0.474972 | 0.273501 | -0.789908 | -0.085745 | 0.868592 | -0.492577 |
| 25 | 0.449834 | -0.315494 | -0.187659 | -0.426842 | -0.875213 | 0.916315 | -0.212134 | -0.503325 | 0.251438 | 0.499009 | 1.750633 | 0.286629 |
| 26 | -0.745212 | -0.457525 | -0.261214 | 1.653228 | 0.471865 | -0.532478 | 3.048431 | -0.344057 | 0.968569 | -1.690268 | 0.186111 | -0.576484 |
| 27 | -1.146960 | 2.737908 | 0.205319 | -0.915593 | 1.224042 | -1.080890 | 2.432039 | -1.475399 | 1.443033 | -0.136290 | -1.161316 | 1.353237 |
| 28 | -0.006670 | 2.177984 | -0.364684 | -0.232426 | 2.164165 | -0.501370 | 0.481222 | -0.462802 | -0.495909 | 0.200246 | -0.925963 | -0.127350 |
| 29 | -1.198149 | 1.245381 | 1.824748 | -0.544122 | 2.094805 | -0.986543 | 1.141795 | -1.276929 | 1.620288 | 0.772653 | -1.183303 | -0.633811 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -0.850302 | -0.813520 | -1.238258 | 0.389340 | -0.820553 | -0.147948 | -0.867381 | -1.187728 | 1.620288 | -0.228859 | 0.676545 | -1.166259 |
| 226 | 1.113887 | -0.637363 | -0.178882 | -0.867049 | -0.180374 | -0.059762 | -1.031280 | 1.075930 | 1.620288 | -0.831212 | -0.536781 | -0.852284 |
| 227 | -0.939216 | -1.028651 | -1.083821 | 0.671288 | -1.104197 | 0.395233 | -0.935964 | -0.545426 | -0.734897 | -1.634988 | 1.750633 | -0.991450 |
| 228 | 0.323318 | -0.538560 | 1.959063 | 0.469737 | -0.928167 | 0.315432 | -0.838357 | 0.553622 | -0.985928 | -0.404420 | 0.017683 | -0.868443 |
| 229 | 0.651351 | 0.768776 | 1.651644 | 0.613579 | -0.672599 | 0.778338 | -0.288893 | 1.735353 | -0.698435 | -0.803492 | 0.749774 | -0.176628 |
| 230 | 1.208263 | 0.590562 | -0.856200 | -0.037362 | -0.890995 | 0.154056 | -0.224548 | 0.060386 | 1.620288 | -0.577590 | 1.081262 | 0.137052 |
| 231 | 1.629732 | -0.424079 | -0.793853 | -0.297522 | 0.876511 | -0.502033 | -0.540340 | 1.735353 | 1.179917 | 0.331082 | -0.186926 | 0.043014 |
| 232 | 1.097162 | 0.374225 | -0.897801 | -0.315126 | 0.016346 | 0.367805 | -0.397202 | 0.034201 | 0.736455 | 1.084098 | 1.111692 | 0.505493 |
| 233 | 1.452404 | -0.818664 | 1.001952 | 0.094142 | -1.157451 | -0.673136 | -1.031650 | -0.681166 | -1.578572 | -1.040029 | 1.750633 | -1.088779 |
| 234 | 0.687938 | 0.363684 | -0.077785 | 1.495170 | -0.946513 | 0.162091 | -0.875163 | -0.486609 | -0.657582 | -0.728212 | 1.750633 | 0.166071 |
| 235 | -0.511818 | -1.019067 | 1.937312 | -0.923999 | -1.199069 | -1.157422 | 1.424573 | -1.506879 | -0.941792 | 1.084098 | -0.598433 | 1.497327 |
| 236 | -0.649452 | 1.110585 | 0.023607 | -0.619494 | -0.351503 | -0.377758 | 0.774664 | -1.037327 | -0.008921 | 1.084098 | -0.300447 | -0.799951 |
| 237 | -0.675917 | 0.864345 | 1.959063 | -0.702632 | 0.767520 | -0.242236 | -0.320118 | -0.889868 | -0.543499 | 0.953653 | -0.295863 | 0.184530 |
| 238 | -0.820946 | 0.480728 | -0.348445 | 1.706293 | -0.634861 | -0.548325 | -0.658463 | -1.446347 | -0.595881 | -1.346009 | 1.750633 | -0.891882 |
| 239 | 0.335654 | -0.570366 | 0.440736 | 2.255028 | -0.777152 | -0.336048 | -0.104033 | 0.504513 | -0.304387 | -0.899407 | 1.750633 | 0.586226 |
| 240 | 0.772849 | -0.288034 | 0.998235 | 2.707124 | 0.694491 | 1.600236 | -0.599878 | 0.863164 | 1.620288 | -0.793363 | 1.076398 | 0.313476 |
| 241 | -0.725775 | -0.253169 | -1.058923 | -0.019515 | -0.367824 | -0.703472 | 0.163373 | 0.093846 | 1.620288 | 0.712877 | -0.410881 | 0.776774 |
| 242 | -0.883133 | 0.370675 | -1.102573 | 0.232760 | -1.047240 | -0.968298 | 0.151912 | -0.346068 | 1.620288 | -0.219994 | -0.255762 | 0.629036 |
| 243 | -0.504299 | -0.158035 | -0.129250 | 0.170764 | 0.127833 | -0.424841 | 0.960604 | 0.067663 | 1.620288 | 0.679838 | 0.005478 | 0.547596 |
| 244 | -0.829496 | 0.743464 | 1.959063 | 0.266679 | 0.653670 | -1.095468 | 1.900161 | 1.007911 | -0.442746 | 0.870766 | -0.246038 | 1.728101 |
| 245 | -1.203285 | 0.455171 | 0.496797 | -0.873183 | 0.629642 | -1.113864 | 1.271226 | 0.542153 | -0.636367 | 1.084098 | -1.140705 | 0.222521 |
| 246 | -1.122600 | -0.442839 | 1.824660 | -0.819762 | 1.050840 | -1.048446 | 1.937596 | 0.131208 | -0.474964 | 0.536040 | -0.800922 | 1.853511 |
| 247 | -1.206944 | -0.795159 | -1.159900 | -0.187089 | 0.233445 | 2.452569 | 0.183293 | 1.253589 | -1.394630 | 0.753640 | -1.083641 | -1.163597 |
| 248 | -0.501382 | -1.030219 | -0.612979 | -0.923999 | -0.608020 | -0.863086 | -0.124132 | -0.944272 | -1.081271 | 1.084098 | -1.170461 | -1.244399 |
| 249 | -0.704093 | -0.985145 | 1.199508 | -0.321552 | 2.164165 | -0.193249 | -0.295412 | -0.394034 | 0.032818 | -1.672473 | -0.724683 | -1.067831 |
| 250 | 0.831957 | -0.173367 | 1.636565 | 1.345345 | -0.989257 | 0.826135 | -0.824412 | -0.072225 | -0.255975 | -1.005500 | 1.750633 | -1.067130 |
| 251 | -0.895156 | -1.022380 | -0.410545 | 2.237273 | -1.199069 | 0.687783 | -1.006468 | -0.694581 | -1.311235 | -1.270219 | 1.750633 | -1.233006 |
| 252 | -0.546320 | -0.631883 | -0.800789 | -0.187107 | -1.179353 | 0.464606 | -0.905192 | -0.113592 | 0.167123 | -0.843254 | 1.750633 | -1.148945 |
| 253 | -0.591349 | -0.947758 | -0.915262 | -0.579179 | 0.089961 | 0.047756 | -0.905554 | -1.413215 | -1.184716 | 1.084098 | 0.384684 | -1.035788 |
| 254 | -0.072947 | -0.912155 | 0.150662 | -0.246506 | -1.198328 | -0.779047 | -1.031650 | 0.022522 | -1.604883 | -1.609572 | 1.750632 | -1.247525 |
255 rows × 12 columns
In [72]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[72]:
[3060.0, 2594.1549165385713, 2309.549481414484, 2089.610498278143, 1959.0566777030967, 1842.9588281368096, 1736.092921360928, 1663.5689730025233, 1609.4951000525748, 1557.5767570007226, 1514.8225721032359, 1447.3960995377222, 1422.027087832329, 1389.3032827223215]
In [73]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[73]:
[<matplotlib.lines.Line2D at 0x1ef6210cef0>]
K=2
In [74]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[74]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [75]:
kmeans_ch.labels_
Out[75]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1])
In [76]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[76]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1])
In [77]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [78]:
X
Out[78]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.369691 | -0.881824 | -0.095656 | -0.923999 | -0.030645 | -0.834931 | -1.031650 | -0.840942 | -0.677716 | 1.084098 | -1.064999 | -1.156623 | 0 | 0 |
| 1 | -0.175875 | -0.403800 | -0.657709 | -0.201259 | 1.691433 | -0.672783 | -0.119944 | -0.440080 | 0.339906 | 1.084098 | 0.504608 | 0.931676 | 0 | 0 |
| 2 | 0.894452 | -0.189794 | 1.959063 | 0.169276 | -0.403611 | -1.036954 | 1.447615 | -0.340767 | -0.846170 | -0.515065 | -0.699878 | 0.032355 | 0 | 0 |
| 3 | 0.060782 | -0.392075 | 0.826233 | -0.048480 | 1.789786 | -0.552163 | 0.121028 | -0.111355 | 0.220614 | 1.084098 | 0.073241 | 1.176257 | 0 | 0 |
| 4 | -1.116536 | -0.923742 | -1.238971 | -0.919598 | 0.313068 | -1.160111 | 1.316032 | -0.700013 | -1.600210 | 1.084098 | -1.072155 | 1.270095 | 0 | 0 |
| 5 | -1.082752 | -0.067472 | -1.142511 | -0.923999 | 0.389201 | -1.061180 | 2.067035 | -0.269819 | -1.620482 | -0.468180 | -0.962315 | 1.853511 | 0 | 0 |
| 6 | -1.202528 | -0.776453 | -1.135820 | -0.773268 | -0.409932 | -0.832224 | -0.631931 | -0.119492 | 1.620288 | 0.162930 | -1.063358 | 0.001987 | 0 | 0 |
| 7 | -1.206944 | 0.500703 | -0.243295 | -0.611928 | 0.262760 | -0.855004 | 0.001441 | -1.579225 | 0.245997 | 1.084098 | -1.197067 | -0.681282 | 0 | 0 |
| 8 | -0.982006 | 0.421831 | 0.984997 | -0.550391 | 0.215104 | -1.100712 | 2.248321 | -0.336428 | 1.282109 | 1.084098 | -0.061910 | 0.480931 | 0 | 0 |
| 9 | 1.703175 | 1.029234 | 1.508859 | 0.964653 | 2.164165 | 0.482073 | -0.794175 | 0.729102 | 0.721867 | 0.439251 | 0.198086 | 1.778147 | 0 | 0 |
| 10 | 1.528139 | 0.898498 | 0.923889 | 0.634045 | 2.016059 | 0.674138 | -0.430188 | 0.558129 | 1.200855 | 0.618822 | 0.291110 | 1.853511 | 0 | 0 |
| 11 | 0.334361 | -0.301383 | -0.450307 | -0.470199 | -0.977542 | 0.863046 | -0.396657 | -0.882307 | 0.259614 | 0.809320 | 1.750633 | 0.245556 | 1 | 0 |
| 12 | 0.597458 | 0.773201 | 0.182265 | 0.104921 | 0.580017 | 0.644184 | 1.433111 | 1.735353 | -0.712181 | -0.818426 | -0.129551 | -0.039236 | 0 | 0 |
| 13 | -0.493625 | 1.341798 | -0.632970 | 2.666081 | 2.015672 | 1.537362 | 2.432631 | 0.500840 | 1.337627 | 1.084098 | -0.069418 | 1.097765 | 0 | 0 |
| 14 | 0.962230 | 0.028408 | 0.059003 | -0.233385 | 1.425585 | 1.226062 | 1.160066 | 1.604723 | 0.272753 | 1.084098 | 0.709073 | 1.356660 | 0 | 0 |
| 15 | -0.843247 | 0.160055 | 1.959063 | -0.354971 | -0.294051 | -0.485118 | -0.796417 | -0.212355 | -0.168152 | -0.782723 | -0.232169 | -0.234956 | 0 | 0 |
| 16 | -0.256403 | 1.322075 | 1.520118 | 0.907583 | 1.032003 | 0.577931 | 0.414295 | 0.551597 | 0.783033 | 1.084098 | 0.088744 | 1.848078 | 0 | 0 |
| 17 | -0.456352 | 2.142046 | 1.959063 | 1.734877 | 1.472675 | 0.985568 | 0.646614 | 0.230853 | 0.051480 | -1.122573 | -0.594948 | 0.587848 | 0 | 0 |
| 18 | -0.637040 | -1.030219 | -1.165495 | -0.606800 | 2.164165 | -1.161299 | -0.882343 | 1.727762 | -1.021803 | -1.420384 | -1.197067 | -1.157909 | 0 | 0 |
| 19 | -1.204564 | -1.022455 | -0.683305 | -0.923999 | -0.935167 | -0.988596 | -0.688535 | 1.735353 | -0.372860 | -1.581696 | -1.197067 | -0.315002 | 0 | 0 |
| 20 | -1.077420 | -0.314272 | 1.036699 | -0.850174 | -0.071202 | -1.104286 | 0.102157 | -0.867378 | -0.464391 | 0.591092 | -1.048902 | 1.853510 | 0 | 0 |
| 21 | -0.494728 | -1.023105 | -0.383945 | -0.918858 | 0.498899 | -1.042513 | -0.072226 | -0.187025 | -0.589871 | 1.084098 | -0.986967 | 0.537859 | 0 | 0 |
| 22 | -0.525116 | 0.187277 | 1.220635 | -0.221678 | 0.589822 | -1.115053 | 0.421737 | 0.656325 | -0.098846 | 1.084098 | -0.740109 | 1.853156 | 0 | 0 |
| 23 | -0.282675 | 0.571926 | -0.333097 | 2.860439 | 1.250860 | 1.434107 | 2.006573 | 0.362041 | 1.620288 | 0.896295 | -0.274184 | 1.257508 | 0 | 0 |
| 24 | 1.898264 | 1.240876 | 0.302806 | -0.427292 | -0.709276 | -0.159183 | -0.474972 | 0.273501 | -0.789908 | -0.085745 | 0.868592 | -0.492577 | 1 | 0 |
| 25 | 0.449834 | -0.315494 | -0.187659 | -0.426842 | -0.875213 | 0.916315 | -0.212134 | -0.503325 | 0.251438 | 0.499009 | 1.750633 | 0.286629 | 1 | 0 |
| 26 | -0.745212 | -0.457525 | -0.261214 | 1.653228 | 0.471865 | -0.532478 | 3.048431 | -0.344057 | 0.968569 | -1.690268 | 0.186111 | -0.576484 | 1 | 0 |
| 27 | -1.146960 | 2.737908 | 0.205319 | -0.915593 | 1.224042 | -1.080890 | 2.432039 | -1.475399 | 1.443033 | -0.136290 | -1.161316 | 1.353237 | 0 | 0 |
| 28 | -0.006670 | 2.177984 | -0.364684 | -0.232426 | 2.164165 | -0.501370 | 0.481222 | -0.462802 | -0.495909 | 0.200246 | -0.925963 | -0.127350 | 0 | 0 |
| 29 | -1.198149 | 1.245381 | 1.824748 | -0.544122 | 2.094805 | -0.986543 | 1.141795 | -1.276929 | 1.620288 | 0.772653 | -1.183303 | -0.633811 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | -0.850302 | -0.813520 | -1.238258 | 0.389340 | -0.820553 | -0.147948 | -0.867381 | -1.187728 | 1.620288 | -0.228859 | 0.676545 | -1.166259 | 1 | 1 |
| 226 | 1.113887 | -0.637363 | -0.178882 | -0.867049 | -0.180374 | -0.059762 | -1.031280 | 1.075930 | 1.620288 | -0.831212 | -0.536781 | -0.852284 | 1 | 1 |
| 227 | -0.939216 | -1.028651 | -1.083821 | 0.671288 | -1.104197 | 0.395233 | -0.935964 | -0.545426 | -0.734897 | -1.634988 | 1.750633 | -0.991450 | 1 | 1 |
| 228 | 0.323318 | -0.538560 | 1.959063 | 0.469737 | -0.928167 | 0.315432 | -0.838357 | 0.553622 | -0.985928 | -0.404420 | 0.017683 | -0.868443 | 1 | 1 |
| 229 | 0.651351 | 0.768776 | 1.651644 | 0.613579 | -0.672599 | 0.778338 | -0.288893 | 1.735353 | -0.698435 | -0.803492 | 0.749774 | -0.176628 | 1 | 1 |
| 230 | 1.208263 | 0.590562 | -0.856200 | -0.037362 | -0.890995 | 0.154056 | -0.224548 | 0.060386 | 1.620288 | -0.577590 | 1.081262 | 0.137052 | 1 | 1 |
| 231 | 1.629732 | -0.424079 | -0.793853 | -0.297522 | 0.876511 | -0.502033 | -0.540340 | 1.735353 | 1.179917 | 0.331082 | -0.186926 | 0.043014 | 0 | 1 |
| 232 | 1.097162 | 0.374225 | -0.897801 | -0.315126 | 0.016346 | 0.367805 | -0.397202 | 0.034201 | 0.736455 | 1.084098 | 1.111692 | 0.505493 | 1 | 1 |
| 233 | 1.452404 | -0.818664 | 1.001952 | 0.094142 | -1.157451 | -0.673136 | -1.031650 | -0.681166 | -1.578572 | -1.040029 | 1.750633 | -1.088779 | 1 | 1 |
| 234 | 0.687938 | 0.363684 | -0.077785 | 1.495170 | -0.946513 | 0.162091 | -0.875163 | -0.486609 | -0.657582 | -0.728212 | 1.750633 | 0.166071 | 1 | 1 |
| 235 | -0.511818 | -1.019067 | 1.937312 | -0.923999 | -1.199069 | -1.157422 | 1.424573 | -1.506879 | -0.941792 | 1.084098 | -0.598433 | 1.497327 | 0 | 1 |
| 236 | -0.649452 | 1.110585 | 0.023607 | -0.619494 | -0.351503 | -0.377758 | 0.774664 | -1.037327 | -0.008921 | 1.084098 | -0.300447 | -0.799951 | 0 | 1 |
| 237 | -0.675917 | 0.864345 | 1.959063 | -0.702632 | 0.767520 | -0.242236 | -0.320118 | -0.889868 | -0.543499 | 0.953653 | -0.295863 | 0.184530 | 0 | 1 |
| 238 | -0.820946 | 0.480728 | -0.348445 | 1.706293 | -0.634861 | -0.548325 | -0.658463 | -1.446347 | -0.595881 | -1.346009 | 1.750633 | -0.891882 | 1 | 1 |
| 239 | 0.335654 | -0.570366 | 0.440736 | 2.255028 | -0.777152 | -0.336048 | -0.104033 | 0.504513 | -0.304387 | -0.899407 | 1.750633 | 0.586226 | 1 | 1 |
| 240 | 0.772849 | -0.288034 | 0.998235 | 2.707124 | 0.694491 | 1.600236 | -0.599878 | 0.863164 | 1.620288 | -0.793363 | 1.076398 | 0.313476 | 1 | 1 |
| 241 | -0.725775 | -0.253169 | -1.058923 | -0.019515 | -0.367824 | -0.703472 | 0.163373 | 0.093846 | 1.620288 | 0.712877 | -0.410881 | 0.776774 | 0 | 1 |
| 242 | -0.883133 | 0.370675 | -1.102573 | 0.232760 | -1.047240 | -0.968298 | 0.151912 | -0.346068 | 1.620288 | -0.219994 | -0.255762 | 0.629036 | 0 | 1 |
| 243 | -0.504299 | -0.158035 | -0.129250 | 0.170764 | 0.127833 | -0.424841 | 0.960604 | 0.067663 | 1.620288 | 0.679838 | 0.005478 | 0.547596 | 0 | 1 |
| 244 | -0.829496 | 0.743464 | 1.959063 | 0.266679 | 0.653670 | -1.095468 | 1.900161 | 1.007911 | -0.442746 | 0.870766 | -0.246038 | 1.728101 | 0 | 1 |
| 245 | -1.203285 | 0.455171 | 0.496797 | -0.873183 | 0.629642 | -1.113864 | 1.271226 | 0.542153 | -0.636367 | 1.084098 | -1.140705 | 0.222521 | 0 | 1 |
| 246 | -1.122600 | -0.442839 | 1.824660 | -0.819762 | 1.050840 | -1.048446 | 1.937596 | 0.131208 | -0.474964 | 0.536040 | -0.800922 | 1.853511 | 0 | 1 |
| 247 | -1.206944 | -0.795159 | -1.159900 | -0.187089 | 0.233445 | 2.452569 | 0.183293 | 1.253589 | -1.394630 | 0.753640 | -1.083641 | -1.163597 | 0 | 1 |
| 248 | -0.501382 | -1.030219 | -0.612979 | -0.923999 | -0.608020 | -0.863086 | -0.124132 | -0.944272 | -1.081271 | 1.084098 | -1.170461 | -1.244399 | 0 | 1 |
| 249 | -0.704093 | -0.985145 | 1.199508 | -0.321552 | 2.164165 | -0.193249 | -0.295412 | -0.394034 | 0.032818 | -1.672473 | -0.724683 | -1.067831 | 0 | 1 |
| 250 | 0.831957 | -0.173367 | 1.636565 | 1.345345 | -0.989257 | 0.826135 | -0.824412 | -0.072225 | -0.255975 | -1.005500 | 1.750633 | -1.067130 | 1 | 1 |
| 251 | -0.895156 | -1.022380 | -0.410545 | 2.237273 | -1.199069 | 0.687783 | -1.006468 | -0.694581 | -1.311235 | -1.270219 | 1.750633 | -1.233006 | 1 | 1 |
| 252 | -0.546320 | -0.631883 | -0.800789 | -0.187107 | -1.179353 | 0.464606 | -0.905192 | -0.113592 | 0.167123 | -0.843254 | 1.750633 | -1.148945 | 1 | 1 |
| 253 | -0.591349 | -0.947758 | -0.915262 | -0.579179 | 0.089961 | 0.047756 | -0.905554 | -1.413215 | -1.184716 | 1.084098 | 0.384684 | -1.035788 | 0 | 1 |
| 254 | -0.072947 | -0.912155 | 0.150662 | -0.246506 | -1.198328 | -0.779047 | -1.031650 | 0.022522 | -1.604883 | -1.609572 | 1.750632 | -1.247525 | 1 | 1 |
255 rows × 14 columns
In [79]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[79]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ef62148208>
In [80]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[2]))
Gramma¶
ANN¶
In [435]:
X = df_n_ps_std_ch[2]
In [436]:
y = df_n_ps[2]['chosen']
In [437]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [438]:
X_train.shape
Out[438]:
(231, 12)
In [85]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [86]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [87]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [88]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'tanh', 'hidden_layer_sizes': (20, 10), 'learning_rate_init': 0.003, 'max_iter': 100}, que permiten obtener un Accuracy de 81.39% y un Kappa del 20.50
Tiempo total: 29.47 minutos
In [439]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20,10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.003
epochs = 100
In [440]:
input_tensor = Input(shape = (n0,))
In [441]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [442]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [443]:
model.summary()
Model: "model_23" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_23 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_72 (Dense) (None, 20) 260 _________________________________________________________________ dense_73 (Dense) (None, 10) 210 _________________________________________________________________ dense_74 (Dense) (None, 1) 11 ================================================================= Total params: 481 Trainable params: 481 Non-trainable params: 0 _________________________________________________________________
In [444]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 231 samples, validate on 78 samples Epoch 1/100 231/231 [==============================] - 0s 805us/step - loss: 0.6917 - accuracy: 0.5714 - val_loss: 0.6342 - val_accuracy: 0.6795 Epoch 2/100 231/231 [==============================] - 0s 65us/step - loss: 0.6302 - accuracy: 0.7013 - val_loss: 0.5826 - val_accuracy: 0.7949 Epoch 3/100 231/231 [==============================] - 0s 61us/step - loss: 0.5899 - accuracy: 0.7706 - val_loss: 0.5455 - val_accuracy: 0.8205 Epoch 4/100 231/231 [==============================] - 0s 82us/step - loss: 0.5610 - accuracy: 0.7792 - val_loss: 0.5164 - val_accuracy: 0.8333 Epoch 5/100 231/231 [==============================] - 0s 78us/step - loss: 0.5344 - accuracy: 0.7965 - val_loss: 0.4901 - val_accuracy: 0.8333 Epoch 6/100 231/231 [==============================] - 0s 74us/step - loss: 0.5117 - accuracy: 0.7879 - val_loss: 0.4738 - val_accuracy: 0.8205 Epoch 7/100 231/231 [==============================] - 0s 56us/step - loss: 0.4959 - accuracy: 0.7792 - val_loss: 0.4605 - val_accuracy: 0.8333 Epoch 8/100 231/231 [==============================] - 0s 61us/step - loss: 0.4828 - accuracy: 0.7879 - val_loss: 0.4486 - val_accuracy: 0.8205 Epoch 9/100 231/231 [==============================] - 0s 52us/step - loss: 0.4786 - accuracy: 0.7879 - val_loss: 0.4402 - val_accuracy: 0.8333 Epoch 10/100 231/231 [==============================] - 0s 61us/step - loss: 0.4704 - accuracy: 0.7879 - val_loss: 0.4366 - val_accuracy: 0.8333 Epoch 11/100 231/231 [==============================] - 0s 52us/step - loss: 0.4685 - accuracy: 0.7879 - val_loss: 0.4392 - val_accuracy: 0.8333 Epoch 12/100 231/231 [==============================] - 0s 61us/step - loss: 0.4619 - accuracy: 0.7922 - val_loss: 0.4381 - val_accuracy: 0.8333 Epoch 13/100 231/231 [==============================] - 0s 61us/step - loss: 0.4587 - accuracy: 0.7922 - val_loss: 0.4367 - val_accuracy: 0.8333 Epoch 14/100 231/231 [==============================] - 0s 61us/step - loss: 0.4537 - accuracy: 0.7922 - val_loss: 0.4337 - val_accuracy: 0.8333 Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.001500000013038516. Epoch 15/100 231/231 [==============================] - 0s 56us/step - loss: 0.4488 - accuracy: 0.8009 - val_loss: 0.4314 - val_accuracy: 0.8333 Epoch 16/100 231/231 [==============================] - 0s 56us/step - loss: 0.4475 - accuracy: 0.7965 - val_loss: 0.4300 - val_accuracy: 0.8462 Epoch 17/100 231/231 [==============================] - 0s 61us/step - loss: 0.4476 - accuracy: 0.7965 - val_loss: 0.4287 - val_accuracy: 0.8462 Epoch 18/100 231/231 [==============================] - 0s 61us/step - loss: 0.4468 - accuracy: 0.7922 - val_loss: 0.4269 - val_accuracy: 0.8462 Epoch 19/100 231/231 [==============================] - 0s 61us/step - loss: 0.4428 - accuracy: 0.7879 - val_loss: 0.4270 - val_accuracy: 0.8462 Epoch 20/100 231/231 [==============================] - 0s 65us/step - loss: 0.4409 - accuracy: 0.7879 - val_loss: 0.4263 - val_accuracy: 0.8462 Epoch 21/100 231/231 [==============================] - 0s 100us/step - loss: 0.4385 - accuracy: 0.7922 - val_loss: 0.4275 - val_accuracy: 0.8333 Epoch 22/100 231/231 [==============================] - 0s 65us/step - loss: 0.4384 - accuracy: 0.7965 - val_loss: 0.4277 - val_accuracy: 0.8333 Epoch 23/100 231/231 [==============================] - 0s 61us/step - loss: 0.4364 - accuracy: 0.7965 - val_loss: 0.4266 - val_accuracy: 0.8333 Epoch 24/100 231/231 [==============================] - 0s 69us/step - loss: 0.4345 - accuracy: 0.7922 - val_loss: 0.4261 - val_accuracy: 0.8333 Epoch 25/100 231/231 [==============================] - 0s 61us/step - loss: 0.4323 - accuracy: 0.7835 - val_loss: 0.4240 - val_accuracy: 0.8462 Epoch 26/100 231/231 [==============================] - 0s 69us/step - loss: 0.4331 - accuracy: 0.7879 - val_loss: 0.4231 - val_accuracy: 0.8462 Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.000750000006519258. Epoch 27/100 231/231 [==============================] - 0s 61us/step - loss: 0.4299 - accuracy: 0.7879 - val_loss: 0.4234 - val_accuracy: 0.8462 Epoch 28/100 231/231 [==============================] - 0s 65us/step - loss: 0.4289 - accuracy: 0.7879 - val_loss: 0.4237 - val_accuracy: 0.8462 Epoch 29/100 231/231 [==============================] - 0s 65us/step - loss: 0.4275 - accuracy: 0.7922 - val_loss: 0.4241 - val_accuracy: 0.8462 Epoch 30/100 231/231 [==============================] - 0s 65us/step - loss: 0.4270 - accuracy: 0.7965 - val_loss: 0.4253 - val_accuracy: 0.8462 Epoch 31/100 231/231 [==============================] - 0s 56us/step - loss: 0.4262 - accuracy: 0.8052 - val_loss: 0.4270 - val_accuracy: 0.8462 Epoch 32/100 231/231 [==============================] - 0s 61us/step - loss: 0.4259 - accuracy: 0.8095 - val_loss: 0.4282 - val_accuracy: 0.8462 Epoch 33/100 231/231 [==============================] - 0s 61us/step - loss: 0.4248 - accuracy: 0.8095 - val_loss: 0.4278 - val_accuracy: 0.8462 Epoch 34/100 231/231 [==============================] - 0s 61us/step - loss: 0.4242 - accuracy: 0.8052 - val_loss: 0.4274 - val_accuracy: 0.8333 Epoch 35/100 231/231 [==============================] - 0s 61us/step - loss: 0.4231 - accuracy: 0.8009 - val_loss: 0.4269 - val_accuracy: 0.8333 Epoch 36/100 231/231 [==============================] - 0s 69us/step - loss: 0.4227 - accuracy: 0.8009 - val_loss: 0.4261 - val_accuracy: 0.8333 Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.000375000003259629. Epoch 37/100 231/231 [==============================] - 0s 56us/step - loss: 0.4215 - accuracy: 0.8009 - val_loss: 0.4259 - val_accuracy: 0.8333 Epoch 38/100 231/231 [==============================] - 0s 78us/step - loss: 0.4210 - accuracy: 0.8009 - val_loss: 0.4253 - val_accuracy: 0.8333 Epoch 39/100 231/231 [==============================] - 0s 87us/step - loss: 0.4205 - accuracy: 0.8009 - val_loss: 0.4246 - val_accuracy: 0.8333 Epoch 40/100 231/231 [==============================] - 0s 65us/step - loss: 0.4196 - accuracy: 0.8009 - val_loss: 0.4241 - val_accuracy: 0.8333 Epoch 41/100 231/231 [==============================] - 0s 65us/step - loss: 0.4193 - accuracy: 0.8052 - val_loss: 0.4236 - val_accuracy: 0.8333 Epoch 42/100 231/231 [==============================] - 0s 78us/step - loss: 0.4188 - accuracy: 0.8052 - val_loss: 0.4235 - val_accuracy: 0.8333 Epoch 43/100 231/231 [==============================] - 0s 74us/step - loss: 0.4184 - accuracy: 0.8052 - val_loss: 0.4243 - val_accuracy: 0.8333 Epoch 44/100 231/231 [==============================] - 0s 65us/step - loss: 0.4180 - accuracy: 0.8052 - val_loss: 0.4244 - val_accuracy: 0.8333 Epoch 45/100 231/231 [==============================] - 0s 65us/step - loss: 0.4177 - accuracy: 0.8009 - val_loss: 0.4237 - val_accuracy: 0.8333 Epoch 46/100 231/231 [==============================] - 0s 65us/step - loss: 0.4174 - accuracy: 0.8009 - val_loss: 0.4235 - val_accuracy: 0.8205 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145. Epoch 47/100 231/231 [==============================] - 0s 65us/step - loss: 0.4170 - accuracy: 0.8009 - val_loss: 0.4232 - val_accuracy: 0.8205 Epoch 48/100 231/231 [==============================] - 0s 69us/step - loss: 0.4166 - accuracy: 0.8009 - val_loss: 0.4231 - val_accuracy: 0.8205 Epoch 49/100 231/231 [==============================] - 0s 65us/step - loss: 0.4164 - accuracy: 0.8009 - val_loss: 0.4232 - val_accuracy: 0.8205 Epoch 50/100 231/231 [==============================] - 0s 65us/step - loss: 0.4161 - accuracy: 0.8009 - val_loss: 0.4231 - val_accuracy: 0.8205 Epoch 51/100 231/231 [==============================] - 0s 61us/step - loss: 0.4159 - accuracy: 0.8009 - val_loss: 0.4232 - val_accuracy: 0.8333 Epoch 52/100 231/231 [==============================] - 0s 61us/step - loss: 0.4158 - accuracy: 0.8052 - val_loss: 0.4233 - val_accuracy: 0.8333 Epoch 53/100 231/231 [==============================] - 0s 61us/step - loss: 0.4155 - accuracy: 0.8052 - val_loss: 0.4236 - val_accuracy: 0.8205 Epoch 54/100 231/231 [==============================] - 0s 56us/step - loss: 0.4153 - accuracy: 0.8052 - val_loss: 0.4236 - val_accuracy: 0.8205 Epoch 55/100 231/231 [==============================] - 0s 61us/step - loss: 0.4151 - accuracy: 0.8009 - val_loss: 0.4235 - val_accuracy: 0.8205 Epoch 56/100 231/231 [==============================] - 0s 61us/step - loss: 0.4149 - accuracy: 0.8009 - val_loss: 0.4235 - val_accuracy: 0.8205 Epoch 00056: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05. Epoch 57/100 231/231 [==============================] - 0s 61us/step - loss: 0.4147 - accuracy: 0.8009 - val_loss: 0.4236 - val_accuracy: 0.8205 Epoch 58/100 231/231 [==============================] - 0s 91us/step - loss: 0.4146 - accuracy: 0.8009 - val_loss: 0.4235 - val_accuracy: 0.8205 Epoch 59/100 231/231 [==============================] - 0s 61us/step - loss: 0.4145 - accuracy: 0.8009 - val_loss: 0.4235 - val_accuracy: 0.8205 Epoch 60/100 231/231 [==============================] - 0s 87us/step - loss: 0.4144 - accuracy: 0.8052 - val_loss: 0.4234 - val_accuracy: 0.8205 Epoch 61/100 231/231 [==============================] - 0s 65us/step - loss: 0.4143 - accuracy: 0.8052 - val_loss: 0.4234 - val_accuracy: 0.8205 Epoch 62/100 231/231 [==============================] - 0s 78us/step - loss: 0.4142 - accuracy: 0.8009 - val_loss: 0.4233 - val_accuracy: 0.8205 Epoch 63/100 231/231 [==============================] - 0s 78us/step - loss: 0.4141 - accuracy: 0.8052 - val_loss: 0.4231 - val_accuracy: 0.8333 Epoch 64/100 231/231 [==============================] - 0s 69us/step - loss: 0.4140 - accuracy: 0.8052 - val_loss: 0.4229 - val_accuracy: 0.8333 Epoch 65/100 231/231 [==============================] - 0s 95us/step - loss: 0.4138 - accuracy: 0.8052 - val_loss: 0.4228 - val_accuracy: 0.8333 Epoch 66/100 231/231 [==============================] - 0s 100us/step - loss: 0.4137 - accuracy: 0.8052 - val_loss: 0.4229 - val_accuracy: 0.8333 Epoch 00066: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05. Epoch 67/100 231/231 [==============================] - 0s 87us/step - loss: 0.4137 - accuracy: 0.8052 - val_loss: 0.4228 - val_accuracy: 0.8333 Epoch 68/100 231/231 [==============================] - 0s 95us/step - loss: 0.4136 - accuracy: 0.8052 - val_loss: 0.4227 - val_accuracy: 0.8333 Epoch 69/100 231/231 [==============================] - 0s 82us/step - loss: 0.4136 - accuracy: 0.8052 - val_loss: 0.4227 - val_accuracy: 0.8333 Epoch 70/100 231/231 [==============================] - 0s 82us/step - loss: 0.4135 - accuracy: 0.8052 - val_loss: 0.4226 - val_accuracy: 0.8333 Epoch 71/100 231/231 [==============================] - 0s 82us/step - loss: 0.4134 - accuracy: 0.8052 - val_loss: 0.4225 - val_accuracy: 0.8333 Epoch 72/100 231/231 [==============================] - 0s 78us/step - loss: 0.4134 - accuracy: 0.8052 - val_loss: 0.4224 - val_accuracy: 0.8333 Epoch 73/100 231/231 [==============================] - 0s 82us/step - loss: 0.4133 - accuracy: 0.8009 - val_loss: 0.4224 - val_accuracy: 0.8333 Epoch 74/100 231/231 [==============================] - 0s 74us/step - loss: 0.4133 - accuracy: 0.8009 - val_loss: 0.4224 - val_accuracy: 0.8205 Epoch 75/100 231/231 [==============================] - 0s 121us/step - loss: 0.4132 - accuracy: 0.8009 - val_loss: 0.4224 - val_accuracy: 0.8205 Epoch 76/100 231/231 [==============================] - 0s 78us/step - loss: 0.4132 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 00076: ReduceLROnPlateau reducing learning rate to 2.3437500203726813e-05. Epoch 77/100 231/231 [==============================] - 0s 82us/step - loss: 0.4131 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 78/100 231/231 [==============================] - 0s 82us/step - loss: 0.4131 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 79/100 231/231 [==============================] - 0s 87us/step - loss: 0.4131 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 80/100 231/231 [==============================] - 0s 74us/step - loss: 0.4130 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 81/100 231/231 [==============================] - 0s 69us/step - loss: 0.4130 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 82/100 231/231 [==============================] - 0s 82us/step - loss: 0.4130 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 83/100 231/231 [==============================] - 0s 87us/step - loss: 0.4129 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 84/100 231/231 [==============================] - 0s 82us/step - loss: 0.4129 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 85/100 231/231 [==============================] - 0s 95us/step - loss: 0.4129 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 86/100 231/231 [==============================] - 0s 91us/step - loss: 0.4129 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 00086: ReduceLROnPlateau reducing learning rate to 1.1718750101863407e-05. Epoch 87/100 231/231 [==============================] - 0s 95us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 88/100 231/231 [==============================] - 0s 104us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 89/100 231/231 [==============================] - 0s 78us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4223 - val_accuracy: 0.8205 Epoch 90/100 231/231 [==============================] - 0s 95us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 91/100 231/231 [==============================] - 0s 78us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 92/100 231/231 [==============================] - 0s 91us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 93/100 231/231 [==============================] - 0s 78us/step - loss: 0.4128 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 94/100 231/231 [==============================] - 0s 78us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 95/100 231/231 [==============================] - 0s 82us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 96/100 231/231 [==============================] - 0s 78us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 00096: ReduceLROnPlateau reducing learning rate to 5.859375050931703e-06. Epoch 97/100 231/231 [==============================] - 0s 74us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 98/100 231/231 [==============================] - 0s 74us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 99/100 231/231 [==============================] - 0s 78us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205 Epoch 100/100 231/231 [==============================] - 0s 91us/step - loss: 0.4127 - accuracy: 0.8009 - val_loss: 0.4222 - val_accuracy: 0.8205
In [445]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [446]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
78/78 [==============================] - 0s 64us/step test loss: 0.42215611384465146, test accuracy: 0.8205128312110901
In [447]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7087053571428572
In [448]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.2155172413793104
KMeans¶
In [100]:
X
Out[100]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.984589 | -0.654155 | -0.792745 | -0.859479 | 0.177345 | -0.665615 | -0.552507 | 0.352420 | -0.300142 | 0.769752 | -0.504858 | 0.181350 |
| 1 | -0.102317 | 1.607758 | 0.006886 | -0.548179 | 0.490499 | -0.344630 | 0.877114 | -0.585463 | 1.429327 | 0.172897 | 0.202254 | 1.373180 |
| 2 | -0.439474 | 2.028691 | -0.353938 | 2.078331 | -1.039184 | -0.467977 | 1.821419 | -1.268548 | -0.701337 | -1.697571 | -0.326854 | 0.472916 |
| 3 | -0.645076 | 0.109508 | -0.651900 | 1.301219 | -0.609256 | -0.080210 | 1.087784 | 0.361091 | 1.429328 | -0.614952 | 0.382530 | 0.101301 |
| 4 | -0.301057 | 0.336491 | 1.322422 | 0.959162 | -0.936732 | -0.362802 | 1.110344 | 0.010511 | 1.429327 | -0.501805 | 0.442290 | -0.322884 |
| 5 | -1.259648 | 0.791285 | 0.394989 | -0.842002 | 0.272018 | -0.902333 | 1.319181 | -0.909587 | 0.270086 | 1.385370 | -1.157401 | -0.786117 |
| 6 | -0.473744 | 0.544499 | 0.562651 | -0.121769 | 0.552950 | -0.248724 | 0.899872 | 0.460196 | 1.152068 | 1.385371 | -0.774641 | -0.707276 |
| 7 | -0.774266 | 0.899101 | 0.080156 | -0.046730 | 0.386542 | -0.597906 | 0.929468 | 0.498309 | 1.429327 | 0.480853 | -0.478808 | -0.428642 |
| 8 | 1.984589 | -0.912964 | 1.574884 | -0.842897 | -0.520036 | -0.919448 | -0.932145 | 1.867087 | -0.601601 | -0.679714 | -0.985387 | -0.175334 |
| 9 | 1.984589 | -0.409991 | 1.175249 | -0.543262 | -0.453426 | -0.170551 | -0.874287 | 0.438503 | -0.647564 | 0.510081 | -0.815886 | -0.260353 |
| 10 | 1.984589 | -1.189009 | -0.443729 | -1.134391 | -0.469034 | -0.729802 | -1.117639 | -0.299959 | -1.382187 | 0.098391 | -1.243653 | -1.042776 |
| 11 | 1.425631 | -0.405095 | 1.943010 | -0.275254 | -0.249374 | 0.219391 | -0.780272 | 2.124774 | -0.587222 | -0.149453 | -0.440426 | -0.501966 |
| 12 | -0.175927 | -1.348663 | -0.477174 | -1.254474 | -0.853911 | 2.667488 | -1.049255 | -0.222134 | -1.773507 | -0.580300 | -0.908675 | -1.449154 |
| 13 | -0.085508 | -0.457470 | 0.184100 | -0.334355 | -0.765637 | 1.103120 | -0.823425 | 2.124774 | -0.182534 | -0.465675 | 1.179407 | -0.204100 |
| 14 | 0.250965 | 2.028690 | -1.244012 | 0.617467 | -0.882089 | 2.044217 | 1.871164 | -1.088962 | -0.995141 | -1.735168 | 0.105923 | -1.470599 |
| 15 | 0.541805 | -0.001425 | -0.925880 | 0.238435 | -0.575240 | 2.198189 | -0.519574 | -0.694298 | 0.048619 | -1.297201 | 1.668396 | -0.729377 |
| 16 | 1.075493 | 1.812588 | -0.957979 | -0.104324 | -0.731780 | 1.782624 | -0.873902 | -0.910773 | 0.252506 | -1.403241 | 1.668396 | -0.957208 |
| 17 | 1.162343 | -1.410603 | 1.107861 | -1.278878 | 0.652589 | -1.189878 | -0.607570 | 2.124774 | -1.773507 | -1.635072 | -1.485428 | 0.776204 |
| 18 | -0.350570 | -0.306746 | 0.672887 | -0.541074 | -0.262104 | -0.270106 | -0.268765 | 2.124774 | -0.322479 | 0.633526 | 0.782517 | 0.912903 |
| 19 | 1.984589 | -1.166218 | -0.302121 | -0.837423 | 1.444010 | -0.977651 | -0.769393 | 0.971765 | -1.024957 | 0.065328 | -0.751049 | 1.582401 |
| 20 | -0.344429 | 0.397628 | 0.492928 | 0.088349 | 1.259038 | -0.375019 | 0.861329 | 0.835025 | 0.519729 | 1.385370 | 0.302340 | 1.587608 |
| 21 | -0.665123 | -0.712314 | -0.800151 | -0.469685 | 0.600390 | -0.337541 | 0.573835 | 0.311324 | 0.473167 | 1.385371 | -0.000823 | 0.495499 |
| 22 | -0.034726 | 0.631798 | 1.392275 | 0.218558 | 0.576512 | -0.244433 | 1.034652 | 0.817298 | 0.794999 | 1.129188 | -0.194205 | 1.762490 |
| 23 | 1.984589 | -0.007008 | 1.098575 | 0.079477 | 0.064847 | 1.686726 | 0.117647 | 1.534220 | -0.572812 | 0.064077 | 1.117174 | 0.106404 |
| 24 | 0.863870 | -0.225984 | 0.529571 | -0.208265 | -0.709105 | 0.215012 | -0.123712 | 2.124775 | 0.286024 | 0.404840 | 0.142872 | 0.545621 |
| 25 | 1.984589 | -0.637932 | 0.518394 | -0.066070 | 0.047258 | 1.915570 | -0.370971 | 1.598931 | -0.707575 | -0.227660 | 0.935568 | -0.410777 |
| 26 | -0.534296 | -0.293829 | -0.533721 | 2.591888 | -0.459134 | 0.157203 | -0.637435 | -0.966788 | -1.041968 | -1.536181 | 1.668394 | -1.235485 |
| 27 | -1.305187 | -1.249514 | -0.918547 | 0.558531 | -0.810228 | -0.527321 | -0.867245 | 1.052965 | 1.429326 | -0.486176 | -1.156630 | -1.447430 |
| 28 | -0.702071 | 2.028691 | -0.850117 | 0.070237 | 1.467057 | -0.296061 | 1.830975 | -0.993617 | 0.591063 | 0.197858 | -0.843315 | 1.002836 |
| 29 | 0.432566 | 0.860284 | -1.227202 | 0.670559 | -1.159673 | 0.418544 | -0.996449 | -0.930246 | 0.373902 | -1.168053 | 1.668396 | -1.329085 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 279 | -1.348839 | 0.139880 | -1.253885 | -1.275537 | -0.280640 | -1.141193 | -0.892432 | -1.344572 | 1.429327 | 0.195292 | -1.485428 | -1.377643 |
| 280 | -0.179872 | 2.028690 | -0.263275 | -0.310922 | 1.069421 | -0.450122 | 2.019519 | -0.993600 | -0.104343 | 0.791611 | -0.318981 | 0.603525 |
| 281 | 0.469201 | -0.567649 | -0.709387 | 0.607882 | -0.765402 | -0.261128 | -0.801727 | 0.179241 | 1.429328 | -0.204750 | 0.800232 | -0.581854 |
| 282 | 0.543320 | -0.456381 | -0.634102 | 0.325918 | -0.664498 | -0.167886 | -0.687116 | -0.079271 | 1.429328 | -0.602810 | 1.035451 | -0.583375 |
| 283 | 0.477794 | -0.769583 | -0.735746 | 1.473847 | -0.762374 | 0.780975 | -0.774212 | 0.728189 | 1.429327 | -0.365422 | 1.597677 | -0.544492 |
| 284 | 0.222631 | -0.766506 | 0.927101 | -0.670166 | 1.411449 | -0.709842 | 0.247985 | 2.124775 | -0.967118 | 0.016153 | -0.867689 | 0.553112 |
| 285 | 1.911041 | -1.385949 | 0.489432 | -1.277915 | 0.744174 | -1.189878 | -0.732335 | 2.124775 | -1.427934 | 0.826977 | -0.913373 | 1.545683 |
| 286 | 0.998963 | -0.323703 | 1.401654 | -0.268532 | 1.829759 | -0.704029 | -0.260658 | 2.124775 | -0.758056 | 0.093732 | -0.465792 | 1.060927 |
| 287 | -0.510470 | -0.039911 | 1.963115 | -0.642206 | 0.369415 | -0.773032 | -0.797277 | -0.297193 | -0.872803 | 1.385370 | -0.488415 | 1.418495 |
| 288 | -0.254650 | 0.178893 | 1.558633 | -0.039151 | 0.587666 | -0.615370 | -0.017793 | 0.430361 | 0.049355 | 1.129887 | -0.078385 | 1.762490 |
| 289 | -0.808854 | -0.341264 | 1.351164 | -0.628500 | 0.452102 | -0.907851 | -0.256562 | 0.160761 | -0.607798 | 0.915654 | -0.519960 | 1.762490 |
| 290 | 1.984589 | -0.481795 | 0.508510 | -0.575951 | -0.779282 | 0.435357 | -0.690793 | 0.480685 | -0.370618 | 0.452005 | -0.553849 | -0.500601 |
| 291 | 0.998287 | -0.217204 | 0.367127 | 0.044444 | -0.456589 | 0.906812 | -0.366517 | 1.231917 | 0.553998 | 1.385370 | 0.680908 | 0.240103 |
| 292 | 0.798451 | -0.538113 | 1.041133 | -0.029389 | -0.595522 | 0.670339 | -0.544354 | 1.434857 | -0.084621 | 1.385370 | -0.330343 | -0.433781 |
| 293 | -0.943702 | 0.737028 | -0.728275 | 1.208260 | -0.076623 | -0.869741 | -0.566060 | -0.607766 | 1.429327 | -0.367929 | -0.514004 | -0.152881 |
| 294 | -0.783173 | 2.028690 | -0.694537 | 0.534761 | 0.700872 | -0.657312 | 0.593459 | -0.865193 | 0.804282 | 0.411026 | -0.455890 | -0.114253 |
| 295 | -1.353539 | 0.236507 | -1.253885 | 0.505804 | 2.532661 | -1.137257 | 2.639114 | -1.344572 | -0.623826 | -1.059108 | -1.485217 | -0.664024 |
| 296 | -0.814494 | -0.644175 | -0.746555 | -0.089905 | 1.458628 | -0.329786 | -0.180452 | -0.280519 | -0.556615 | 1.385371 | -0.562439 | 0.215725 |
| 297 | -1.328790 | -0.867949 | -1.235248 | -1.143782 | -0.824785 | -1.189878 | 0.173069 | -1.209548 | -0.747695 | 0.835692 | -1.224526 | 1.762490 |
| 298 | -0.492926 | 0.191754 | 0.111709 | -0.525002 | 1.181452 | -0.578483 | 1.076554 | 0.005169 | -0.283691 | 1.385370 | -0.639443 | 0.647082 |
| 299 | -0.170158 | 1.429318 | 0.352435 | -0.389217 | 0.358405 | 0.174620 | 1.813232 | 0.463259 | 1.229264 | 1.385371 | 0.316814 | 0.934409 |
| 300 | -0.735769 | 1.192104 | 0.781909 | -0.385227 | 1.393252 | -0.120730 | 0.406288 | -0.281463 | 0.586122 | 1.385371 | -0.245906 | 0.425382 |
| 301 | -0.019554 | 1.160350 | 0.498593 | -0.197460 | 2.311770 | 0.265997 | 2.684564 | -0.481065 | -0.409788 | 0.174802 | -0.763379 | 0.571031 |
| 302 | 1.400364 | -1.135263 | -1.004589 | -0.298465 | -1.155209 | -0.890738 | -0.994393 | 0.530779 | 1.429327 | -0.891882 | 0.281780 | -1.060312 |
| 303 | 0.689113 | -1.410603 | -1.223892 | 2.775857 | -1.269849 | 0.014549 | -1.133868 | 1.868258 | 0.446129 | -1.735168 | -0.154717 | -1.470599 |
| 304 | 0.922331 | -1.397035 | -0.464459 | 0.077595 | -1.183685 | -0.909669 | -1.052482 | 0.619950 | 1.429327 | -1.524369 | -0.354957 | -1.435964 |
| 305 | 0.750767 | -0.421453 | -0.860282 | 0.506761 | -0.768372 | 0.918258 | -0.755944 | -0.644496 | 1.257549 | -0.509585 | 1.668396 | -0.681109 |
| 306 | 0.961272 | -0.181417 | -0.598543 | 1.120314 | -0.794696 | 0.953980 | -0.773921 | -0.695204 | 1.429327 | 0.092550 | 1.404805 | -0.528387 |
| 307 | -0.525215 | 1.812541 | -0.767815 | -0.363920 | 1.274741 | 0.174788 | 0.980181 | -0.048116 | -0.124203 | 1.385370 | -0.185668 | 0.000363 |
| 308 | -1.312658 | -1.394783 | 1.272388 | -1.278878 | -1.269849 | 2.667487 | -1.096080 | 1.886802 | -1.773507 | 1.153659 | 0.035789 | -1.470599 |
309 rows × 12 columns
In [101]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[101]:
[3708.0, 2878.224392689417, 2409.0313876777413, 2245.2681343684408, 2096.2730590487927, 1980.9081903971983, 1884.1812168752008, 1782.3304172674002, 1692.3482768291478, 1629.7037390768492, 1586.8745376225052, 1530.5845891254487, 1455.503322263105, 1429.3496938708681]
In [102]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[102]:
[<matplotlib.lines.Line2D at 0x1ef622f5c18>]
K=2
In [103]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[103]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [104]:
kmeans_ch.labels_
Out[104]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
1])
In [105]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[105]:
array([1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
1])
In [106]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [107]:
X
Out[107]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.984589 | -0.654155 | -0.792745 | -0.859479 | 0.177345 | -0.665615 | -0.552507 | 0.352420 | -0.300142 | 0.769752 | -0.504858 | 0.181350 | 1 | 0 |
| 1 | -0.102317 | 1.607758 | 0.006886 | -0.548179 | 0.490499 | -0.344630 | 0.877114 | -0.585463 | 1.429327 | 0.172897 | 0.202254 | 1.373180 | 1 | 0 |
| 2 | -0.439474 | 2.028691 | -0.353938 | 2.078331 | -1.039184 | -0.467977 | 1.821419 | -1.268548 | -0.701337 | -1.697571 | -0.326854 | 0.472916 | 0 | 0 |
| 3 | -0.645076 | 0.109508 | -0.651900 | 1.301219 | -0.609256 | -0.080210 | 1.087784 | 0.361091 | 1.429328 | -0.614952 | 0.382530 | 0.101301 | 0 | 0 |
| 4 | -0.301057 | 0.336491 | 1.322422 | 0.959162 | -0.936732 | -0.362802 | 1.110344 | 0.010511 | 1.429327 | -0.501805 | 0.442290 | -0.322884 | 0 | 0 |
| 5 | -1.259648 | 0.791285 | 0.394989 | -0.842002 | 0.272018 | -0.902333 | 1.319181 | -0.909587 | 0.270086 | 1.385370 | -1.157401 | -0.786117 | 1 | 0 |
| 6 | -0.473744 | 0.544499 | 0.562651 | -0.121769 | 0.552950 | -0.248724 | 0.899872 | 0.460196 | 1.152068 | 1.385371 | -0.774641 | -0.707276 | 1 | 0 |
| 7 | -0.774266 | 0.899101 | 0.080156 | -0.046730 | 0.386542 | -0.597906 | 0.929468 | 0.498309 | 1.429327 | 0.480853 | -0.478808 | -0.428642 | 1 | 0 |
| 8 | 1.984589 | -0.912964 | 1.574884 | -0.842897 | -0.520036 | -0.919448 | -0.932145 | 1.867087 | -0.601601 | -0.679714 | -0.985387 | -0.175334 | 1 | 0 |
| 9 | 1.984589 | -0.409991 | 1.175249 | -0.543262 | -0.453426 | -0.170551 | -0.874287 | 0.438503 | -0.647564 | 0.510081 | -0.815886 | -0.260353 | 1 | 0 |
| 10 | 1.984589 | -1.189009 | -0.443729 | -1.134391 | -0.469034 | -0.729802 | -1.117639 | -0.299959 | -1.382187 | 0.098391 | -1.243653 | -1.042776 | 1 | 0 |
| 11 | 1.425631 | -0.405095 | 1.943010 | -0.275254 | -0.249374 | 0.219391 | -0.780272 | 2.124774 | -0.587222 | -0.149453 | -0.440426 | -0.501966 | 1 | 0 |
| 12 | -0.175927 | -1.348663 | -0.477174 | -1.254474 | -0.853911 | 2.667488 | -1.049255 | -0.222134 | -1.773507 | -0.580300 | -0.908675 | -1.449154 | 0 | 0 |
| 13 | -0.085508 | -0.457470 | 0.184100 | -0.334355 | -0.765637 | 1.103120 | -0.823425 | 2.124774 | -0.182534 | -0.465675 | 1.179407 | -0.204100 | 0 | 0 |
| 14 | 0.250965 | 2.028690 | -1.244012 | 0.617467 | -0.882089 | 2.044217 | 1.871164 | -1.088962 | -0.995141 | -1.735168 | 0.105923 | -1.470599 | 0 | 0 |
| 15 | 0.541805 | -0.001425 | -0.925880 | 0.238435 | -0.575240 | 2.198189 | -0.519574 | -0.694298 | 0.048619 | -1.297201 | 1.668396 | -0.729377 | 0 | 0 |
| 16 | 1.075493 | 1.812588 | -0.957979 | -0.104324 | -0.731780 | 1.782624 | -0.873902 | -0.910773 | 0.252506 | -1.403241 | 1.668396 | -0.957208 | 0 | 0 |
| 17 | 1.162343 | -1.410603 | 1.107861 | -1.278878 | 0.652589 | -1.189878 | -0.607570 | 2.124774 | -1.773507 | -1.635072 | -1.485428 | 0.776204 | 1 | 0 |
| 18 | -0.350570 | -0.306746 | 0.672887 | -0.541074 | -0.262104 | -0.270106 | -0.268765 | 2.124774 | -0.322479 | 0.633526 | 0.782517 | 0.912903 | 1 | 0 |
| 19 | 1.984589 | -1.166218 | -0.302121 | -0.837423 | 1.444010 | -0.977651 | -0.769393 | 0.971765 | -1.024957 | 0.065328 | -0.751049 | 1.582401 | 1 | 0 |
| 20 | -0.344429 | 0.397628 | 0.492928 | 0.088349 | 1.259038 | -0.375019 | 0.861329 | 0.835025 | 0.519729 | 1.385370 | 0.302340 | 1.587608 | 1 | 0 |
| 21 | -0.665123 | -0.712314 | -0.800151 | -0.469685 | 0.600390 | -0.337541 | 0.573835 | 0.311324 | 0.473167 | 1.385371 | -0.000823 | 0.495499 | 1 | 0 |
| 22 | -0.034726 | 0.631798 | 1.392275 | 0.218558 | 0.576512 | -0.244433 | 1.034652 | 0.817298 | 0.794999 | 1.129188 | -0.194205 | 1.762490 | 1 | 0 |
| 23 | 1.984589 | -0.007008 | 1.098575 | 0.079477 | 0.064847 | 1.686726 | 0.117647 | 1.534220 | -0.572812 | 0.064077 | 1.117174 | 0.106404 | 0 | 0 |
| 24 | 0.863870 | -0.225984 | 0.529571 | -0.208265 | -0.709105 | 0.215012 | -0.123712 | 2.124775 | 0.286024 | 0.404840 | 0.142872 | 0.545621 | 1 | 0 |
| 25 | 1.984589 | -0.637932 | 0.518394 | -0.066070 | 0.047258 | 1.915570 | -0.370971 | 1.598931 | -0.707575 | -0.227660 | 0.935568 | -0.410777 | 0 | 0 |
| 26 | -0.534296 | -0.293829 | -0.533721 | 2.591888 | -0.459134 | 0.157203 | -0.637435 | -0.966788 | -1.041968 | -1.536181 | 1.668394 | -1.235485 | 0 | 0 |
| 27 | -1.305187 | -1.249514 | -0.918547 | 0.558531 | -0.810228 | -0.527321 | -0.867245 | 1.052965 | 1.429326 | -0.486176 | -1.156630 | -1.447430 | 0 | 0 |
| 28 | -0.702071 | 2.028691 | -0.850117 | 0.070237 | 1.467057 | -0.296061 | 1.830975 | -0.993617 | 0.591063 | 0.197858 | -0.843315 | 1.002836 | 1 | 0 |
| 29 | 0.432566 | 0.860284 | -1.227202 | 0.670559 | -1.159673 | 0.418544 | -0.996449 | -0.930246 | 0.373902 | -1.168053 | 1.668396 | -1.329085 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 279 | -1.348839 | 0.139880 | -1.253885 | -1.275537 | -0.280640 | -1.141193 | -0.892432 | -1.344572 | 1.429327 | 0.195292 | -1.485428 | -1.377643 | 1 | 1 |
| 280 | -0.179872 | 2.028690 | -0.263275 | -0.310922 | 1.069421 | -0.450122 | 2.019519 | -0.993600 | -0.104343 | 0.791611 | -0.318981 | 0.603525 | 1 | 1 |
| 281 | 0.469201 | -0.567649 | -0.709387 | 0.607882 | -0.765402 | -0.261128 | -0.801727 | 0.179241 | 1.429328 | -0.204750 | 0.800232 | -0.581854 | 0 | 1 |
| 282 | 0.543320 | -0.456381 | -0.634102 | 0.325918 | -0.664498 | -0.167886 | -0.687116 | -0.079271 | 1.429328 | -0.602810 | 1.035451 | -0.583375 | 0 | 1 |
| 283 | 0.477794 | -0.769583 | -0.735746 | 1.473847 | -0.762374 | 0.780975 | -0.774212 | 0.728189 | 1.429327 | -0.365422 | 1.597677 | -0.544492 | 0 | 1 |
| 284 | 0.222631 | -0.766506 | 0.927101 | -0.670166 | 1.411449 | -0.709842 | 0.247985 | 2.124775 | -0.967118 | 0.016153 | -0.867689 | 0.553112 | 1 | 1 |
| 285 | 1.911041 | -1.385949 | 0.489432 | -1.277915 | 0.744174 | -1.189878 | -0.732335 | 2.124775 | -1.427934 | 0.826977 | -0.913373 | 1.545683 | 1 | 1 |
| 286 | 0.998963 | -0.323703 | 1.401654 | -0.268532 | 1.829759 | -0.704029 | -0.260658 | 2.124775 | -0.758056 | 0.093732 | -0.465792 | 1.060927 | 1 | 1 |
| 287 | -0.510470 | -0.039911 | 1.963115 | -0.642206 | 0.369415 | -0.773032 | -0.797277 | -0.297193 | -0.872803 | 1.385370 | -0.488415 | 1.418495 | 1 | 1 |
| 288 | -0.254650 | 0.178893 | 1.558633 | -0.039151 | 0.587666 | -0.615370 | -0.017793 | 0.430361 | 0.049355 | 1.129887 | -0.078385 | 1.762490 | 1 | 1 |
| 289 | -0.808854 | -0.341264 | 1.351164 | -0.628500 | 0.452102 | -0.907851 | -0.256562 | 0.160761 | -0.607798 | 0.915654 | -0.519960 | 1.762490 | 1 | 1 |
| 290 | 1.984589 | -0.481795 | 0.508510 | -0.575951 | -0.779282 | 0.435357 | -0.690793 | 0.480685 | -0.370618 | 0.452005 | -0.553849 | -0.500601 | 1 | 1 |
| 291 | 0.998287 | -0.217204 | 0.367127 | 0.044444 | -0.456589 | 0.906812 | -0.366517 | 1.231917 | 0.553998 | 1.385370 | 0.680908 | 0.240103 | 0 | 1 |
| 292 | 0.798451 | -0.538113 | 1.041133 | -0.029389 | -0.595522 | 0.670339 | -0.544354 | 1.434857 | -0.084621 | 1.385370 | -0.330343 | -0.433781 | 1 | 1 |
| 293 | -0.943702 | 0.737028 | -0.728275 | 1.208260 | -0.076623 | -0.869741 | -0.566060 | -0.607766 | 1.429327 | -0.367929 | -0.514004 | -0.152881 | 0 | 1 |
| 294 | -0.783173 | 2.028690 | -0.694537 | 0.534761 | 0.700872 | -0.657312 | 0.593459 | -0.865193 | 0.804282 | 0.411026 | -0.455890 | -0.114253 | 1 | 1 |
| 295 | -1.353539 | 0.236507 | -1.253885 | 0.505804 | 2.532661 | -1.137257 | 2.639114 | -1.344572 | -0.623826 | -1.059108 | -1.485217 | -0.664024 | 1 | 1 |
| 296 | -0.814494 | -0.644175 | -0.746555 | -0.089905 | 1.458628 | -0.329786 | -0.180452 | -0.280519 | -0.556615 | 1.385371 | -0.562439 | 0.215725 | 1 | 1 |
| 297 | -1.328790 | -0.867949 | -1.235248 | -1.143782 | -0.824785 | -1.189878 | 0.173069 | -1.209548 | -0.747695 | 0.835692 | -1.224526 | 1.762490 | 1 | 1 |
| 298 | -0.492926 | 0.191754 | 0.111709 | -0.525002 | 1.181452 | -0.578483 | 1.076554 | 0.005169 | -0.283691 | 1.385370 | -0.639443 | 0.647082 | 1 | 1 |
| 299 | -0.170158 | 1.429318 | 0.352435 | -0.389217 | 0.358405 | 0.174620 | 1.813232 | 0.463259 | 1.229264 | 1.385371 | 0.316814 | 0.934409 | 1 | 1 |
| 300 | -0.735769 | 1.192104 | 0.781909 | -0.385227 | 1.393252 | -0.120730 | 0.406288 | -0.281463 | 0.586122 | 1.385371 | -0.245906 | 0.425382 | 1 | 1 |
| 301 | -0.019554 | 1.160350 | 0.498593 | -0.197460 | 2.311770 | 0.265997 | 2.684564 | -0.481065 | -0.409788 | 0.174802 | -0.763379 | 0.571031 | 1 | 1 |
| 302 | 1.400364 | -1.135263 | -1.004589 | -0.298465 | -1.155209 | -0.890738 | -0.994393 | 0.530779 | 1.429327 | -0.891882 | 0.281780 | -1.060312 | 0 | 1 |
| 303 | 0.689113 | -1.410603 | -1.223892 | 2.775857 | -1.269849 | 0.014549 | -1.133868 | 1.868258 | 0.446129 | -1.735168 | -0.154717 | -1.470599 | 0 | 1 |
| 304 | 0.922331 | -1.397035 | -0.464459 | 0.077595 | -1.183685 | -0.909669 | -1.052482 | 0.619950 | 1.429327 | -1.524369 | -0.354957 | -1.435964 | 0 | 1 |
| 305 | 0.750767 | -0.421453 | -0.860282 | 0.506761 | -0.768372 | 0.918258 | -0.755944 | -0.644496 | 1.257549 | -0.509585 | 1.668396 | -0.681109 | 0 | 1 |
| 306 | 0.961272 | -0.181417 | -0.598543 | 1.120314 | -0.794696 | 0.953980 | -0.773921 | -0.695204 | 1.429327 | 0.092550 | 1.404805 | -0.528387 | 0 | 1 |
| 307 | -0.525215 | 1.812541 | -0.767815 | -0.363920 | 1.274741 | 0.174788 | 0.980181 | -0.048116 | -0.124203 | 1.385370 | -0.185668 | 0.000363 | 1 | 1 |
| 308 | -1.312658 | -1.394783 | 1.272388 | -1.278878 | -1.269849 | 2.667487 | -1.096080 | 1.886802 | -1.773507 | 1.153659 | 0.035789 | -1.470599 | 1 | 1 |
309 rows × 14 columns
In [108]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[108]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ef62325320>
In [109]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[3]))
Hotel Marrakech¶
ANN¶
In [449]:
X = df_n_ps_std_ch[3]
In [450]:
y = df_n_ps[3]['chosen']
In [451]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [452]:
X_train.shape
Out[452]:
(139, 12)
In [114]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [115]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [116]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [117]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (30, 20, 10), 'learning_rate_init': 0.005, 'max_iter': 400}, que permiten obtener un Accuracy de 74.82% y un Kappa del 36.52
Tiempo total: 20.79 minutos
In [453]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [30,20,10]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.005
epochs = 400
In [454]:
input_tensor = Input(shape = (n0,))
In [455]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [456]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [457]:
model.summary()
Model: "model_24" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_24 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_75 (Dense) (None, 30) 390 _________________________________________________________________ dense_76 (Dense) (None, 20) 620 _________________________________________________________________ dense_77 (Dense) (None, 10) 210 _________________________________________________________________ dense_78 (Dense) (None, 1) 11 ================================================================= Total params: 1,231 Trainable params: 1,231 Non-trainable params: 0 _________________________________________________________________
In [458]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 139 samples, validate on 47 samples Epoch 1/400 139/139 [==============================] - 0s 2ms/step - loss: 0.6839 - accuracy: 0.5612 - val_loss: 0.6643 - val_accuracy: 0.6596 Epoch 2/400 139/139 [==============================] - 0s 101us/step - loss: 0.6139 - accuracy: 0.6978 - val_loss: 0.6457 - val_accuracy: 0.5745 Epoch 3/400 139/139 [==============================] - 0s 86us/step - loss: 0.5802 - accuracy: 0.7050 - val_loss: 0.6362 - val_accuracy: 0.6383 Epoch 4/400 139/139 [==============================] - 0s 79us/step - loss: 0.5585 - accuracy: 0.7050 - val_loss: 0.6262 - val_accuracy: 0.6809 Epoch 5/400 139/139 [==============================] - 0s 79us/step - loss: 0.5379 - accuracy: 0.7266 - val_loss: 0.6363 - val_accuracy: 0.7234 Epoch 6/400 139/139 [==============================] - 0s 86us/step - loss: 0.5218 - accuracy: 0.7554 - val_loss: 0.6389 - val_accuracy: 0.7021 Epoch 7/400 139/139 [==============================] - 0s 72us/step - loss: 0.5042 - accuracy: 0.7410 - val_loss: 0.6527 - val_accuracy: 0.6809 Epoch 8/400 139/139 [==============================] - 0s 86us/step - loss: 0.4917 - accuracy: 0.7770 - val_loss: 0.6756 - val_accuracy: 0.6809 Epoch 9/400 139/139 [==============================] - 0s 86us/step - loss: 0.4787 - accuracy: 0.7986 - val_loss: 0.6979 - val_accuracy: 0.6170 Epoch 10/400 139/139 [==============================] - 0s 79us/step - loss: 0.4664 - accuracy: 0.8201 - val_loss: 0.6851 - val_accuracy: 0.6170 Epoch 11/400 139/139 [==============================] - 0s 93us/step - loss: 0.4505 - accuracy: 0.8345 - val_loss: 0.6736 - val_accuracy: 0.6170 Epoch 12/400 139/139 [==============================] - 0s 101us/step - loss: 0.4432 - accuracy: 0.8273 - val_loss: 0.6798 - val_accuracy: 0.6809 Epoch 13/400 139/139 [==============================] - 0s 86us/step - loss: 0.4199 - accuracy: 0.8129 - val_loss: 0.6860 - val_accuracy: 0.6809 Epoch 14/400 139/139 [==============================] - 0s 79us/step - loss: 0.4032 - accuracy: 0.8273 - val_loss: 0.6889 - val_accuracy: 0.6170 Epoch 15/400 139/139 [==============================] - 0s 79us/step - loss: 0.3848 - accuracy: 0.8489 - val_loss: 0.6918 - val_accuracy: 0.6383 Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455. Epoch 16/400 139/139 [==============================] - 0s 86us/step - loss: 0.3686 - accuracy: 0.8489 - val_loss: 0.7019 - val_accuracy: 0.6170 Epoch 17/400 139/139 [==============================] - 0s 72us/step - loss: 0.3604 - accuracy: 0.8489 - val_loss: 0.7117 - val_accuracy: 0.6170 Epoch 18/400 139/139 [==============================] - 0s 79us/step - loss: 0.3539 - accuracy: 0.8777 - val_loss: 0.7183 - val_accuracy: 0.6170 Epoch 19/400 139/139 [==============================] - 0s 79us/step - loss: 0.3441 - accuracy: 0.8777 - val_loss: 0.7249 - val_accuracy: 0.6170 Epoch 20/400 139/139 [==============================] - 0s 72us/step - loss: 0.3356 - accuracy: 0.8705 - val_loss: 0.7232 - val_accuracy: 0.6170 Epoch 21/400 139/139 [==============================] - 0s 79us/step - loss: 0.3259 - accuracy: 0.8561 - val_loss: 0.7256 - val_accuracy: 0.6383 Epoch 22/400 139/139 [==============================] - 0s 79us/step - loss: 0.3194 - accuracy: 0.8777 - val_loss: 0.7288 - val_accuracy: 0.6383 Epoch 23/400 139/139 [==============================] - 0s 72us/step - loss: 0.3080 - accuracy: 0.8777 - val_loss: 0.7311 - val_accuracy: 0.6383 Epoch 24/400 139/139 [==============================] - 0s 79us/step - loss: 0.2987 - accuracy: 0.8849 - val_loss: 0.7261 - val_accuracy: 0.6383 Epoch 25/400 139/139 [==============================] - 0s 72us/step - loss: 0.2921 - accuracy: 0.8993 - val_loss: 0.7177 - val_accuracy: 0.6170 Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228. Epoch 26/400 139/139 [==============================] - 0s 86us/step - loss: 0.2848 - accuracy: 0.8921 - val_loss: 0.7105 - val_accuracy: 0.6596 Epoch 27/400 139/139 [==============================] - 0s 129us/step - loss: 0.2794 - accuracy: 0.8921 - val_loss: 0.7090 - val_accuracy: 0.6809 Epoch 28/400 139/139 [==============================] - 0s 101us/step - loss: 0.2732 - accuracy: 0.8993 - val_loss: 0.7176 - val_accuracy: 0.6596 Epoch 29/400 139/139 [==============================] - 0s 79us/step - loss: 0.2676 - accuracy: 0.9065 - val_loss: 0.7270 - val_accuracy: 0.6596 Epoch 30/400 139/139 [==============================] - 0s 86us/step - loss: 0.2633 - accuracy: 0.9065 - val_loss: 0.7343 - val_accuracy: 0.6596 Epoch 31/400 139/139 [==============================] - 0s 79us/step - loss: 0.2585 - accuracy: 0.9065 - val_loss: 0.7410 - val_accuracy: 0.6383 Epoch 32/400 139/139 [==============================] - 0s 137us/step - loss: 0.2539 - accuracy: 0.8993 - val_loss: 0.7384 - val_accuracy: 0.6596 Epoch 33/400 139/139 [==============================] - 0s 93us/step - loss: 0.2484 - accuracy: 0.9065 - val_loss: 0.7357 - val_accuracy: 0.6596 Epoch 34/400 139/139 [==============================] - 0s 79us/step - loss: 0.2447 - accuracy: 0.9065 - val_loss: 0.7319 - val_accuracy: 0.6596 Epoch 35/400 139/139 [==============================] - 0s 79us/step - loss: 0.2404 - accuracy: 0.9137 - val_loss: 0.7283 - val_accuracy: 0.6596 Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.0006249999860301614. Epoch 36/400 139/139 [==============================] - 0s 86us/step - loss: 0.2355 - accuracy: 0.9137 - val_loss: 0.7289 - val_accuracy: 0.6596 Epoch 37/400 139/139 [==============================] - 0s 72us/step - loss: 0.2330 - accuracy: 0.9137 - val_loss: 0.7297 - val_accuracy: 0.6596 Epoch 38/400 139/139 [==============================] - 0s 86us/step - loss: 0.2305 - accuracy: 0.9209 - val_loss: 0.7305 - val_accuracy: 0.6596 Epoch 39/400 139/139 [==============================] - 0s 86us/step - loss: 0.2285 - accuracy: 0.9209 - val_loss: 0.7322 - val_accuracy: 0.6596 Epoch 40/400 139/139 [==============================] - 0s 72us/step - loss: 0.2262 - accuracy: 0.9281 - val_loss: 0.7341 - val_accuracy: 0.6383 Epoch 41/400 139/139 [==============================] - 0s 79us/step - loss: 0.2233 - accuracy: 0.9281 - val_loss: 0.7386 - val_accuracy: 0.6383 Epoch 42/400 139/139 [==============================] - 0s 79us/step - loss: 0.2221 - accuracy: 0.9209 - val_loss: 0.7464 - val_accuracy: 0.6596 Epoch 43/400 139/139 [==============================] - 0s 72us/step - loss: 0.2197 - accuracy: 0.9209 - val_loss: 0.7529 - val_accuracy: 0.6596 Epoch 44/400 139/139 [==============================] - 0s 86us/step - loss: 0.2173 - accuracy: 0.9209 - val_loss: 0.7492 - val_accuracy: 0.6383 Epoch 45/400 139/139 [==============================] - 0s 72us/step - loss: 0.2152 - accuracy: 0.9209 - val_loss: 0.7426 - val_accuracy: 0.6596 Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0003124999930150807. Epoch 46/400 139/139 [==============================] - 0s 72us/step - loss: 0.2123 - accuracy: 0.9353 - val_loss: 0.7405 - val_accuracy: 0.6596 Epoch 47/400 139/139 [==============================] - 0s 72us/step - loss: 0.2114 - accuracy: 0.9353 - val_loss: 0.7371 - val_accuracy: 0.6596 Epoch 48/400 139/139 [==============================] - 0s 72us/step - loss: 0.2107 - accuracy: 0.9496 - val_loss: 0.7365 - val_accuracy: 0.6809 Epoch 49/400 139/139 [==============================] - 0s 72us/step - loss: 0.2092 - accuracy: 0.9496 - val_loss: 0.7379 - val_accuracy: 0.6809 Epoch 50/400 139/139 [==============================] - 0s 72us/step - loss: 0.2079 - accuracy: 0.9496 - val_loss: 0.7397 - val_accuracy: 0.6809 Epoch 51/400 139/139 [==============================] - 0s 72us/step - loss: 0.2068 - accuracy: 0.9496 - val_loss: 0.7412 - val_accuracy: 0.6596 Epoch 52/400 139/139 [==============================] - 0s 72us/step - loss: 0.2058 - accuracy: 0.9496 - val_loss: 0.7400 - val_accuracy: 0.6596 Epoch 53/400 139/139 [==============================] - 0s 79us/step - loss: 0.2046 - accuracy: 0.9496 - val_loss: 0.7398 - val_accuracy: 0.6596 Epoch 54/400 139/139 [==============================] - 0s 79us/step - loss: 0.2037 - accuracy: 0.9496 - val_loss: 0.7395 - val_accuracy: 0.6596 Epoch 55/400 139/139 [==============================] - 0s 86us/step - loss: 0.2026 - accuracy: 0.9496 - val_loss: 0.7387 - val_accuracy: 0.6596 Epoch 00055: ReduceLROnPlateau reducing learning rate to 0.00015624999650754035. Epoch 56/400 139/139 [==============================] - 0s 108us/step - loss: 0.2014 - accuracy: 0.9496 - val_loss: 0.7387 - val_accuracy: 0.6596 Epoch 57/400 139/139 [==============================] - 0s 115us/step - loss: 0.2007 - accuracy: 0.9496 - val_loss: 0.7393 - val_accuracy: 0.6596 Epoch 58/400 139/139 [==============================] - 0s 101us/step - loss: 0.2001 - accuracy: 0.9496 - val_loss: 0.7398 - val_accuracy: 0.6596 Epoch 59/400 139/139 [==============================] - 0s 86us/step - loss: 0.1997 - accuracy: 0.9496 - val_loss: 0.7410 - val_accuracy: 0.6596 Epoch 60/400 139/139 [==============================] - 0s 86us/step - loss: 0.1991 - accuracy: 0.9496 - val_loss: 0.7421 - val_accuracy: 0.6596 Epoch 61/400 139/139 [==============================] - 0s 79us/step - loss: 0.1985 - accuracy: 0.9496 - val_loss: 0.7424 - val_accuracy: 0.6596 Epoch 62/400 139/139 [==============================] - 0s 86us/step - loss: 0.1980 - accuracy: 0.9496 - val_loss: 0.7435 - val_accuracy: 0.6596 Epoch 63/400 139/139 [==============================] - 0s 79us/step - loss: 0.1975 - accuracy: 0.9496 - val_loss: 0.7443 - val_accuracy: 0.6596 Epoch 64/400 139/139 [==============================] - 0s 86us/step - loss: 0.1969 - accuracy: 0.9496 - val_loss: 0.7453 - val_accuracy: 0.6596 Epoch 65/400 139/139 [==============================] - 0s 79us/step - loss: 0.1964 - accuracy: 0.9496 - val_loss: 0.7454 - val_accuracy: 0.6596 Epoch 00065: ReduceLROnPlateau reducing learning rate to 7.812499825377017e-05. Epoch 66/400 139/139 [==============================] - 0s 79us/step - loss: 0.1959 - accuracy: 0.9496 - val_loss: 0.7459 - val_accuracy: 0.6383 Epoch 67/400 139/139 [==============================] - 0s 86us/step - loss: 0.1957 - accuracy: 0.9496 - val_loss: 0.7464 - val_accuracy: 0.6596 Epoch 68/400 139/139 [==============================] - 0s 79us/step - loss: 0.1953 - accuracy: 0.9496 - val_loss: 0.7463 - val_accuracy: 0.6383 Epoch 69/400 139/139 [==============================] - 0s 79us/step - loss: 0.1951 - accuracy: 0.9496 - val_loss: 0.7458 - val_accuracy: 0.6596 Epoch 70/400 139/139 [==============================] - 0s 79us/step - loss: 0.1948 - accuracy: 0.9496 - val_loss: 0.7459 - val_accuracy: 0.6596 Epoch 71/400 139/139 [==============================] - 0s 72us/step - loss: 0.1946 - accuracy: 0.9496 - val_loss: 0.7456 - val_accuracy: 0.6383 Epoch 72/400 139/139 [==============================] - 0s 72us/step - loss: 0.1943 - accuracy: 0.9496 - val_loss: 0.7457 - val_accuracy: 0.6383 Epoch 73/400 139/139 [==============================] - 0s 79us/step - loss: 0.1940 - accuracy: 0.9496 - val_loss: 0.7464 - val_accuracy: 0.6383 Epoch 74/400 139/139 [==============================] - 0s 72us/step - loss: 0.1937 - accuracy: 0.9496 - val_loss: 0.7473 - val_accuracy: 0.6596 Epoch 75/400 139/139 [==============================] - 0s 72us/step - loss: 0.1935 - accuracy: 0.9496 - val_loss: 0.7474 - val_accuracy: 0.6596 Epoch 00075: ReduceLROnPlateau reducing learning rate to 3.9062499126885086e-05. Epoch 76/400 139/139 [==============================] - 0s 101us/step - loss: 0.1932 - accuracy: 0.9496 - val_loss: 0.7476 - val_accuracy: 0.6596 Epoch 77/400 139/139 [==============================] - 0s 101us/step - loss: 0.1932 - accuracy: 0.9496 - val_loss: 0.7481 - val_accuracy: 0.6596 Epoch 78/400 139/139 [==============================] - 0s 93us/step - loss: 0.1930 - accuracy: 0.9496 - val_loss: 0.7483 - val_accuracy: 0.6596 Epoch 79/400 139/139 [==============================] - 0s 86us/step - loss: 0.1928 - accuracy: 0.9496 - val_loss: 0.7483 - val_accuracy: 0.6596 Epoch 80/400 139/139 [==============================] - 0s 72us/step - loss: 0.1927 - accuracy: 0.9496 - val_loss: 0.7483 - val_accuracy: 0.6596 Epoch 81/400 139/139 [==============================] - 0s 72us/step - loss: 0.1926 - accuracy: 0.9496 - val_loss: 0.7483 - val_accuracy: 0.6596 Epoch 82/400 139/139 [==============================] - 0s 86us/step - loss: 0.1925 - accuracy: 0.9496 - val_loss: 0.7482 - val_accuracy: 0.6596 Epoch 83/400 139/139 [==============================] - 0s 86us/step - loss: 0.1923 - accuracy: 0.9496 - val_loss: 0.7482 - val_accuracy: 0.6596 Epoch 84/400 139/139 [==============================] - 0s 79us/step - loss: 0.1922 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 85/400 139/139 [==============================] - 0s 79us/step - loss: 0.1920 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 00085: ReduceLROnPlateau reducing learning rate to 1.9531249563442543e-05. Epoch 86/400 139/139 [==============================] - 0s 72us/step - loss: 0.1919 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 87/400 139/139 [==============================] - 0s 72us/step - loss: 0.1919 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 88/400 139/139 [==============================] - 0s 72us/step - loss: 0.1918 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 89/400 139/139 [==============================] - 0s 115us/step - loss: 0.1917 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 90/400 139/139 [==============================] - 0s 108us/step - loss: 0.1916 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 91/400 139/139 [==============================] - 0s 86us/step - loss: 0.1916 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 92/400 139/139 [==============================] - 0s 86us/step - loss: 0.1915 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 93/400 139/139 [==============================] - 0s 79us/step - loss: 0.1914 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 94/400 139/139 [==============================] - 0s 86us/step - loss: 0.1914 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 95/400 139/139 [==============================] - 0s 72us/step - loss: 0.1913 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 00095: ReduceLROnPlateau reducing learning rate to 9.765624781721272e-06. Epoch 96/400 139/139 [==============================] - 0s 79us/step - loss: 0.1912 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 97/400 139/139 [==============================] - 0s 79us/step - loss: 0.1912 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 98/400 139/139 [==============================] - 0s 93us/step - loss: 0.1912 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 99/400 139/139 [==============================] - 0s 79us/step - loss: 0.1911 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 100/400 139/139 [==============================] - 0s 79us/step - loss: 0.1911 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 101/400 139/139 [==============================] - 0s 79us/step - loss: 0.1911 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 102/400 139/139 [==============================] - 0s 72us/step - loss: 0.1910 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 103/400 139/139 [==============================] - 0s 79us/step - loss: 0.1910 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 104/400 139/139 [==============================] - 0s 79us/step - loss: 0.1910 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 105/400 139/139 [==============================] - 0s 108us/step - loss: 0.1909 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00105: ReduceLROnPlateau reducing learning rate to 4.882812390860636e-06. Epoch 106/400 139/139 [==============================] - 0s 79us/step - loss: 0.1909 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 107/400 139/139 [==============================] - 0s 72us/step - loss: 0.1909 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 108/400 139/139 [==============================] - 0s 79us/step - loss: 0.1909 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 109/400 139/139 [==============================] - 0s 79us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 110/400 139/139 [==============================] - 0s 79us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 111/400 139/139 [==============================] - 0s 72us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 112/400 139/139 [==============================] - 0s 72us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 113/400 139/139 [==============================] - 0s 72us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 114/400 139/139 [==============================] - 0s 72us/step - loss: 0.1908 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 115/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00115: ReduceLROnPlateau reducing learning rate to 2.441406195430318e-06. Epoch 116/400 139/139 [==============================] - 0s 79us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7486 - val_accuracy: 0.6596 Epoch 117/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 118/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 119/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 120/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 121/400 139/139 [==============================] - 0s 115us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 122/400 139/139 [==============================] - 0s 93us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 123/400 139/139 [==============================] - 0s 79us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 124/400 139/139 [==============================] - 0s 72us/step - loss: 0.1907 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 125/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 00125: ReduceLROnPlateau reducing learning rate to 1.220703097715159e-06. Epoch 126/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 127/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7488 - val_accuracy: 0.6596 Epoch 128/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 129/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 130/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 131/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 132/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 133/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 134/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 135/400 139/139 [==============================] - 0s 108us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00135: ReduceLROnPlateau reducing learning rate to 6.103515488575795e-07. Epoch 136/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 137/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 138/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 139/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 140/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 141/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 142/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 143/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 144/400 139/139 [==============================] - 0s 65us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 145/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00145: ReduceLROnPlateau reducing learning rate to 3.0517577442878974e-07. Epoch 146/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 147/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 148/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 149/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 150/400 139/139 [==============================] - 0s 65us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 151/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 152/400 139/139 [==============================] - 0s 122us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 153/400 139/139 [==============================] - 0s 122us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 154/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 155/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00155: ReduceLROnPlateau reducing learning rate to 1.5258788721439487e-07. Epoch 156/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 157/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 158/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 159/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 160/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 161/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 162/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 163/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 164/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 165/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00165: ReduceLROnPlateau reducing learning rate to 7.629394360719743e-08. Epoch 166/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 167/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 168/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 169/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 170/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 171/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 172/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 173/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 174/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 175/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00175: ReduceLROnPlateau reducing learning rate to 3.814697180359872e-08. Epoch 176/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 177/400 139/139 [==============================] - 0s 122us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 178/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 179/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 180/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 181/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 182/400 139/139 [==============================] - 0s 129us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 183/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 184/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 185/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00185: ReduceLROnPlateau reducing learning rate to 1.907348590179936e-08. Epoch 186/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 187/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 188/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 189/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 190/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 191/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 192/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 193/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 194/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 195/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00195: ReduceLROnPlateau reducing learning rate to 9.53674295089968e-09. Epoch 196/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 197/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 198/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 199/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 200/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 201/400 139/139 [==============================] - 0s 108us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 202/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 203/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 204/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 205/400 139/139 [==============================] - 0s 137us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00205: ReduceLROnPlateau reducing learning rate to 4.76837147544984e-09. Epoch 206/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 207/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 208/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 209/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 210/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 211/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 212/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 213/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 214/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 215/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00215: ReduceLROnPlateau reducing learning rate to 2.38418573772492e-09. Epoch 216/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 217/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 218/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 219/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 220/400 139/139 [==============================] - 0s 65us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 221/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 222/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 223/400 139/139 [==============================] - 0s 115us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 224/400 139/139 [==============================] - 0s 108us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 225/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00225: ReduceLROnPlateau reducing learning rate to 1.19209286886246e-09. Epoch 226/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 227/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 228/400 139/139 [==============================] - 0s 108us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 229/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 230/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 231/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 232/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 233/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 234/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 235/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00235: ReduceLROnPlateau reducing learning rate to 5.9604643443123e-10. Epoch 236/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 237/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 238/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 239/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 240/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 241/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 242/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 243/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 244/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 245/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00245: ReduceLROnPlateau reducing learning rate to 2.98023217215615e-10. Epoch 246/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 247/400 139/139 [==============================] - 0s 115us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 248/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 249/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 250/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 251/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 252/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 253/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 254/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 255/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00255: ReduceLROnPlateau reducing learning rate to 1.490116086078075e-10. Epoch 256/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 257/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 258/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 259/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 260/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 261/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 262/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 263/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 264/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 265/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00265: ReduceLROnPlateau reducing learning rate to 7.450580430390374e-11. Epoch 266/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 267/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 268/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 269/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 270/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 271/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 272/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 273/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 274/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 275/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00275: ReduceLROnPlateau reducing learning rate to 3.725290215195187e-11. Epoch 276/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 277/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 278/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 279/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 280/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 281/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 282/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 283/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 284/400 139/139 [==============================] - 0s 65us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 285/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00285: ReduceLROnPlateau reducing learning rate to 1.8626451075975936e-11. Epoch 286/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 287/400 139/139 [==============================] - 0s 94us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 288/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 289/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 290/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 291/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 292/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 293/400 139/139 [==============================] - 0s 115us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 294/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 295/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00295: ReduceLROnPlateau reducing learning rate to 9.313225537987968e-12. Epoch 296/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 297/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 298/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 299/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 300/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 301/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 302/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 303/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 304/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 305/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00305: ReduceLROnPlateau reducing learning rate to 4.656612768993984e-12. Epoch 306/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 307/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 308/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 309/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 310/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 311/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 312/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 313/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 314/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 315/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00315: ReduceLROnPlateau reducing learning rate to 2.328306384496992e-12. Epoch 316/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 317/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 318/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 319/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 320/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 321/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 322/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 323/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 324/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 325/400 139/139 [==============================] - 0s 108us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00325: ReduceLROnPlateau reducing learning rate to 1.164153192248496e-12. Epoch 326/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 327/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 328/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 329/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 330/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 331/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 332/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 333/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 334/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 335/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00335: ReduceLROnPlateau reducing learning rate to 5.82076596124248e-13. Epoch 336/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 337/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 338/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 339/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 340/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 341/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 342/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 343/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 344/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 345/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00345: ReduceLROnPlateau reducing learning rate to 2.91038298062124e-13. Epoch 346/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 347/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 348/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 349/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 350/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 351/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 352/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 353/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 354/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 355/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00355: ReduceLROnPlateau reducing learning rate to 1.45519149031062e-13. Epoch 356/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 357/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 358/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 359/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 360/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 361/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 362/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 363/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 364/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 365/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00365: ReduceLROnPlateau reducing learning rate to 7.2759574515531e-14. Epoch 366/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 367/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 368/400 139/139 [==============================] - 0s 65us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 369/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 370/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 371/400 139/139 [==============================] - 0s 115us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 372/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 373/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 374/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 375/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00375: ReduceLROnPlateau reducing learning rate to 3.63797872577655e-14. Epoch 376/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 377/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 378/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 379/400 139/139 [==============================] - 0s 137us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 380/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 381/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 382/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 383/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 384/400 139/139 [==============================] - 0s 129us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 385/400 139/139 [==============================] - 0s 86us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00385: ReduceLROnPlateau reducing learning rate to 1.818989362888275e-14. Epoch 386/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 387/400 139/139 [==============================] - 0s 93us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 388/400 139/139 [==============================] - 0s 115us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 389/400 139/139 [==============================] - 0s 101us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 390/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 391/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 392/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 393/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 394/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 395/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 00395: ReduceLROnPlateau reducing learning rate to 9.094946814441375e-15. Epoch 396/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 397/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 398/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 399/400 139/139 [==============================] - 0s 72us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596 Epoch 400/400 139/139 [==============================] - 0s 79us/step - loss: 0.1906 - accuracy: 0.9496 - val_loss: 0.7487 - val_accuracy: 0.6596
In [459]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 400)
In [460]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
47/47 [==============================] - 0s 85us/step test loss: 0.7487381544518978, test accuracy: 0.6595744490623474
In [461]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.6145833333333334
In [462]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.15695067264573992
KMeans¶
In [128]:
X
Out[128]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.599304 | -1.060396 | 0.465659 | 0.511787 | 1.705341 | -0.372782 | -0.636913 | 1.449092 | 0.180180 | 1.423244 | -0.953744 | -0.200195 |
| 1 | -0.906389 | 1.573440 | 1.673148 | 0.108573 | 0.942015 | -0.855402 | 0.537225 | -0.893282 | -1.001412 | -0.464244 | -1.251319 | -0.454972 |
| 2 | -0.149325 | 0.716481 | -1.280822 | -1.008531 | -1.103704 | -1.077175 | -0.797686 | 0.153926 | 0.441860 | -1.161482 | -0.398909 | 1.830174 |
| 3 | -0.569330 | 0.172306 | -1.126575 | -0.291817 | -0.013676 | -0.722243 | -0.124954 | -1.269880 | -1.213759 | -1.266152 | -0.895866 | 1.830174 |
| 4 | -1.256063 | -0.200780 | -1.280822 | -0.770807 | -1.163484 | -1.044094 | -0.860176 | -1.494914 | -1.397245 | -1.430862 | -0.986514 | 1.830173 |
| 5 | 1.498382 | 0.070778 | 1.241647 | 0.806722 | 0.329740 | 1.002855 | -0.403863 | -0.253624 | -1.031682 | -0.886917 | 1.207114 | -0.113310 |
| 6 | 1.498381 | -0.356738 | 0.435175 | 0.467927 | 1.384000 | 1.966674 | 0.658895 | -0.657164 | -0.969663 | -0.907198 | 0.630731 | -0.244667 |
| 7 | -1.119602 | 2.289616 | 0.868130 | -0.906024 | 1.400639 | -0.225230 | 2.092267 | -0.746990 | 0.637557 | -0.699149 | -0.755684 | -0.485588 |
| 8 | 1.498382 | 0.306863 | -0.233871 | -0.413790 | -1.099299 | -0.508942 | -0.858685 | -0.496630 | -1.256396 | -1.344641 | 0.136594 | -0.642192 |
| 9 | -0.564178 | 0.625076 | -0.818535 | -0.891287 | 0.347738 | -0.712095 | 0.252699 | 0.647252 | 0.527548 | 1.252301 | 0.765167 | 1.830174 |
| 10 | -0.778882 | 1.298999 | 1.673148 | 1.062410 | 0.576150 | 0.140006 | 1.558658 | -0.784362 | -0.261035 | -0.030554 | -0.181206 | 0.766608 |
| 11 | -0.644689 | 1.907389 | 0.724477 | 1.040904 | 2.334063 | 0.466749 | 2.569439 | -0.430087 | 1.103736 | -0.797487 | -0.756174 | -0.052327 |
| 12 | -0.726607 | 0.659282 | 1.063080 | -0.773481 | 0.736899 | -0.854707 | -0.181454 | 0.624941 | 1.157900 | 1.423244 | 0.545411 | 0.884626 |
| 13 | 0.038859 | 1.272019 | -0.111205 | -0.210854 | -0.227700 | -0.708715 | 1.758175 | -0.238102 | 1.439851 | 0.256944 | 0.095708 | -0.202718 |
| 14 | -1.028647 | 1.835461 | 0.117175 | -0.135897 | 1.231759 | -0.367566 | 2.569439 | -0.533125 | 1.077953 | -0.124864 | -0.328656 | -0.256960 |
| 15 | -1.170073 | -1.108093 | 1.429803 | -0.936214 | 0.176597 | -1.037196 | -0.638081 | 2.479776 | -0.735019 | -0.444317 | -1.223897 | -0.027713 |
| 16 | 0.568533 | 0.319544 | 0.717878 | -1.026562 | 0.703422 | -1.077175 | 0.341163 | -1.256313 | -1.126493 | 1.423244 | -1.294186 | -0.649362 |
| 17 | 0.818260 | -0.108057 | 1.673148 | 0.213826 | 2.169898 | -0.176880 | -0.324327 | 0.079245 | -0.325110 | 0.292708 | -0.639169 | 1.253784 |
| 18 | -1.346310 | -1.140106 | -1.280822 | -1.026562 | -0.843277 | -0.873943 | -0.950796 | -1.073565 | -0.253110 | 1.423244 | -1.292263 | -1.229049 |
| 19 | 1.250159 | -1.050602 | -1.222878 | -0.928517 | 0.793165 | 0.458629 | -0.589907 | 0.276552 | -1.106710 | 1.423244 | -1.096356 | 0.241921 |
| 20 | 0.572797 | 2.142771 | 1.581524 | 2.479349 | 0.473394 | -0.093008 | -0.084823 | -1.267718 | -1.284687 | -1.432013 | -1.251386 | -0.638749 |
| 21 | 0.862369 | 2.221963 | 1.673148 | -0.555264 | -0.741332 | -0.892991 | 0.221351 | -0.116798 | 0.378493 | 1.217874 | -1.085435 | -0.256045 |
| 22 | 1.498382 | -0.404560 | 0.195068 | -0.943467 | 0.085181 | -0.851680 | -0.552763 | 1.808182 | -0.441278 | 0.067722 | -0.458537 | 1.662711 |
| 23 | -1.364025 | 1.371234 | 1.673148 | -0.497512 | -0.157640 | -0.979363 | 0.415864 | -1.388894 | -1.148743 | 1.407283 | -1.247653 | -1.119587 |
| 24 | 1.498382 | -0.141656 | 1.241642 | 0.351671 | -0.814862 | -0.540976 | -0.626294 | -0.876743 | -0.616984 | -0.782913 | -0.866230 | 0.810379 |
| 25 | 1.498382 | -0.280382 | 1.532544 | 1.674414 | -0.727241 | -0.446776 | -0.604067 | 0.307172 | 0.222362 | -0.541751 | 0.720031 | 0.083232 |
| 26 | 1.075665 | -0.107428 | 1.673148 | 2.096608 | -0.226236 | 0.860102 | 0.163175 | 0.326457 | 0.203441 | -0.281441 | -0.236211 | 1.166053 |
| 27 | 0.540581 | 0.054204 | 0.505362 | -0.931144 | 0.436468 | -0.842085 | 0.277074 | 2.168789 | -0.135488 | 0.863810 | -0.521048 | 1.830173 |
| 28 | -0.980545 | -1.054168 | -1.263653 | -0.377459 | -1.133370 | -0.845439 | 0.110858 | -1.019562 | 0.100180 | -0.493484 | 0.325493 | 1.830174 |
| 29 | -0.441960 | 1.036991 | -0.878755 | 1.298082 | -0.666326 | 0.845380 | 1.113303 | -0.376141 | 1.439851 | -0.664701 | 1.224927 | 1.272102 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 156 | 0.964566 | -0.478675 | 0.600961 | -1.000674 | -0.743610 | -0.499950 | -0.696044 | -0.174243 | -0.686952 | 1.423244 | -1.030797 | -1.114829 |
| 157 | 0.270654 | 0.971091 | 0.132712 | 0.510175 | 0.258442 | 1.104875 | 0.136485 | 1.451829 | 1.439851 | -0.382326 | 0.865175 | -0.288952 |
| 158 | 0.388178 | -0.776533 | -1.121244 | -0.867688 | -0.827641 | -0.776813 | 0.973880 | -1.056551 | -0.596814 | 1.082405 | 0.005738 | 1.830173 |
| 159 | -0.409488 | -0.518845 | -0.902126 | -0.775313 | -0.624732 | -0.869850 | -0.153020 | -0.306394 | -0.007331 | 1.423244 | 0.011478 | 1.109037 |
| 160 | -1.188703 | -0.210659 | -0.626345 | -0.893545 | -0.918629 | -0.029765 | 2.569439 | -1.381276 | -1.323691 | -0.262947 | -1.017177 | 0.947571 |
| 161 | -1.476495 | -1.140106 | -1.266686 | -1.019916 | -1.162501 | 0.086770 | -0.874030 | -0.598831 | 1.439851 | -1.144185 | -1.294186 | -1.186653 |
| 162 | -1.075963 | -1.140106 | 0.768537 | -1.026562 | 2.565083 | 1.775966 | -0.950796 | -1.453844 | -1.349718 | 1.423244 | -0.660846 | -0.569048 |
| 163 | -1.142157 | -1.140106 | -0.917613 | -0.900746 | 2.578150 | -1.071687 | -0.937734 | -1.420511 | -1.345736 | 0.130051 | -1.058466 | -1.165382 |
| 164 | -0.756762 | -0.149815 | 1.673148 | 0.274589 | 0.009055 | 0.630144 | -0.689021 | 1.604745 | -1.053633 | 0.098821 | 0.463522 | -0.747055 |
| 165 | 0.429405 | -0.093236 | 1.263826 | 0.077597 | 0.114344 | -0.750943 | -0.299174 | 1.420985 | -0.848138 | 1.423244 | 0.247779 | 0.052884 |
| 166 | 0.553018 | -0.191462 | 1.673148 | -0.349285 | -0.196236 | -0.932266 | -0.717123 | -0.672655 | -1.120795 | -0.026811 | -0.537879 | -0.807588 |
| 167 | 0.997841 | 1.111726 | 1.673148 | 0.509323 | 1.012646 | -0.110920 | 1.607274 | 0.848930 | 0.336229 | 0.759005 | 0.460918 | 1.680913 |
| 168 | 1.281068 | 0.266174 | 0.446982 | 0.357357 | 0.348808 | -0.209890 | 0.845374 | 1.311409 | 0.971748 | 1.423244 | 0.410051 | 1.807237 |
| 169 | 0.013523 | -0.761110 | -1.031584 | -0.832016 | 0.088734 | -0.963108 | -0.407038 | -1.026311 | -0.384022 | 0.115756 | -0.062208 | 1.830174 |
| 170 | 0.666873 | -0.889641 | 1.409534 | 0.478248 | 0.640324 | -0.028408 | -0.137784 | -1.195061 | -1.343049 | -1.104665 | -0.345889 | 1.830173 |
| 171 | -0.179155 | -0.647105 | 1.673148 | 0.514384 | 0.469814 | -0.052937 | -0.435133 | -1.056954 | -1.372424 | -1.219127 | -0.571403 | 1.303219 |
| 172 | -0.698104 | -1.140106 | 1.673147 | 1.002618 | -1.030539 | 0.062021 | -0.950796 | -0.862502 | -1.410550 | -1.459509 | 0.007704 | -1.271527 |
| 173 | -0.221070 | -1.120261 | 0.098266 | -1.026027 | -1.172739 | -0.397041 | -0.950796 | 0.162128 | 0.351550 | 1.423244 | 0.924487 | -1.024174 |
| 174 | -0.987264 | -1.140106 | 1.430551 | 0.148205 | -1.172739 | -0.974773 | -0.950796 | 0.046605 | 0.349775 | 1.423244 | 1.009924 | -1.168197 |
| 175 | -0.001472 | 2.289616 | -0.469230 | -0.117523 | 0.087383 | -1.038762 | -0.386872 | -1.069931 | 0.726815 | 1.374128 | 0.589319 | 0.225042 |
| 176 | 0.759267 | 2.289616 | -0.807487 | -0.605891 | -0.207466 | -0.122817 | -0.928360 | -1.465473 | -0.403582 | 0.164132 | 0.091145 | 1.237470 |
| 177 | 0.303330 | 1.071917 | 0.410221 | 0.816208 | 0.386825 | -0.293228 | -0.059633 | 0.466962 | 1.121183 | 1.357331 | 1.812099 | 1.148304 |
| 178 | 0.371415 | 0.428878 | -0.437216 | 0.367689 | -0.713660 | 1.561886 | -0.345821 | -0.608625 | 0.845040 | 0.018040 | 1.812099 | -0.795570 |
| 179 | -1.106214 | 0.339351 | -0.739020 | -0.842920 | -0.830001 | -0.625489 | 0.083218 | 1.521028 | 1.439851 | 0.093070 | 1.538478 | -0.976780 |
| 180 | 0.970965 | 1.730559 | 1.300739 | 1.716251 | 2.578151 | 0.794509 | -0.890606 | -1.133358 | -1.410550 | -1.452853 | -1.269329 | -1.265541 |
| 181 | -1.092848 | -1.140019 | -1.014068 | -0.937922 | 2.397303 | 0.274619 | 0.231215 | 0.010249 | -0.572052 | 0.204326 | -1.044505 | 1.830173 |
| 182 | -0.432669 | -1.140106 | -1.275255 | -0.740960 | 2.578150 | -1.060066 | 1.058297 | -0.120367 | -1.278125 | -1.425308 | -1.294186 | 0.144247 |
| 183 | -1.222408 | -1.109984 | 0.115563 | -1.017839 | -0.845053 | -0.886348 | 0.708798 | 1.447822 | 0.072199 | 1.423244 | -0.469073 | 0.751450 |
| 184 | 1.498382 | 0.114899 | 0.573155 | -0.852483 | -0.059147 | 1.559801 | -0.088275 | 0.489614 | 0.106407 | 1.089262 | -0.583791 | 0.017900 |
| 185 | -0.252632 | -0.228432 | -0.156977 | 2.073566 | 1.013665 | 2.254737 | -0.135625 | -0.232305 | -0.538284 | -0.820780 | 0.474282 | -0.303243 |
186 rows × 12 columns
In [129]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[129]:
[2232.0, 1887.86126663073, 1710.7260050049272, 1558.3692212048186, 1444.9563111647813, 1360.8980768251936, 1285.0938986929364, 1227.1073156803282, 1175.9593811616949, 1123.763719209633, 1080.6180773747733, 1058.1006766601422, 1021.9523094285516, 986.031006954783]
In [130]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[130]:
[<matplotlib.lines.Line2D at 0x1ef63d7d128>]
K=2
In [131]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[131]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [132]:
kmeans_ch.labels_
Out[132]:
array([1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 0])
In [133]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[133]:
array([1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 0])
In [134]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [135]:
X
Out[135]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.599304 | -1.060396 | 0.465659 | 0.511787 | 1.705341 | -0.372782 | -0.636913 | 1.449092 | 0.180180 | 1.423244 | -0.953744 | -0.200195 | 1 | 0 |
| 1 | -0.906389 | 1.573440 | 1.673148 | 0.108573 | 0.942015 | -0.855402 | 0.537225 | -0.893282 | -1.001412 | -0.464244 | -1.251319 | -0.454972 | 1 | 0 |
| 2 | -0.149325 | 0.716481 | -1.280822 | -1.008531 | -1.103704 | -1.077175 | -0.797686 | 0.153926 | 0.441860 | -1.161482 | -0.398909 | 1.830174 | 1 | 0 |
| 3 | -0.569330 | 0.172306 | -1.126575 | -0.291817 | -0.013676 | -0.722243 | -0.124954 | -1.269880 | -1.213759 | -1.266152 | -0.895866 | 1.830174 | 1 | 0 |
| 4 | -1.256063 | -0.200780 | -1.280822 | -0.770807 | -1.163484 | -1.044094 | -0.860176 | -1.494914 | -1.397245 | -1.430862 | -0.986514 | 1.830173 | 1 | 0 |
| 5 | 1.498382 | 0.070778 | 1.241647 | 0.806722 | 0.329740 | 1.002855 | -0.403863 | -0.253624 | -1.031682 | -0.886917 | 1.207114 | -0.113310 | 0 | 0 |
| 6 | 1.498381 | -0.356738 | 0.435175 | 0.467927 | 1.384000 | 1.966674 | 0.658895 | -0.657164 | -0.969663 | -0.907198 | 0.630731 | -0.244667 | 0 | 0 |
| 7 | -1.119602 | 2.289616 | 0.868130 | -0.906024 | 1.400639 | -0.225230 | 2.092267 | -0.746990 | 0.637557 | -0.699149 | -0.755684 | -0.485588 | 0 | 0 |
| 8 | 1.498382 | 0.306863 | -0.233871 | -0.413790 | -1.099299 | -0.508942 | -0.858685 | -0.496630 | -1.256396 | -1.344641 | 0.136594 | -0.642192 | 1 | 0 |
| 9 | -0.564178 | 0.625076 | -0.818535 | -0.891287 | 0.347738 | -0.712095 | 0.252699 | 0.647252 | 0.527548 | 1.252301 | 0.765167 | 1.830174 | 1 | 0 |
| 10 | -0.778882 | 1.298999 | 1.673148 | 1.062410 | 0.576150 | 0.140006 | 1.558658 | -0.784362 | -0.261035 | -0.030554 | -0.181206 | 0.766608 | 0 | 0 |
| 11 | -0.644689 | 1.907389 | 0.724477 | 1.040904 | 2.334063 | 0.466749 | 2.569439 | -0.430087 | 1.103736 | -0.797487 | -0.756174 | -0.052327 | 0 | 0 |
| 12 | -0.726607 | 0.659282 | 1.063080 | -0.773481 | 0.736899 | -0.854707 | -0.181454 | 0.624941 | 1.157900 | 1.423244 | 0.545411 | 0.884626 | 1 | 0 |
| 13 | 0.038859 | 1.272019 | -0.111205 | -0.210854 | -0.227700 | -0.708715 | 1.758175 | -0.238102 | 1.439851 | 0.256944 | 0.095708 | -0.202718 | 0 | 0 |
| 14 | -1.028647 | 1.835461 | 0.117175 | -0.135897 | 1.231759 | -0.367566 | 2.569439 | -0.533125 | 1.077953 | -0.124864 | -0.328656 | -0.256960 | 0 | 0 |
| 15 | -1.170073 | -1.108093 | 1.429803 | -0.936214 | 0.176597 | -1.037196 | -0.638081 | 2.479776 | -0.735019 | -0.444317 | -1.223897 | -0.027713 | 1 | 0 |
| 16 | 0.568533 | 0.319544 | 0.717878 | -1.026562 | 0.703422 | -1.077175 | 0.341163 | -1.256313 | -1.126493 | 1.423244 | -1.294186 | -0.649362 | 1 | 0 |
| 17 | 0.818260 | -0.108057 | 1.673148 | 0.213826 | 2.169898 | -0.176880 | -0.324327 | 0.079245 | -0.325110 | 0.292708 | -0.639169 | 1.253784 | 1 | 0 |
| 18 | -1.346310 | -1.140106 | -1.280822 | -1.026562 | -0.843277 | -0.873943 | -0.950796 | -1.073565 | -0.253110 | 1.423244 | -1.292263 | -1.229049 | 1 | 0 |
| 19 | 1.250159 | -1.050602 | -1.222878 | -0.928517 | 0.793165 | 0.458629 | -0.589907 | 0.276552 | -1.106710 | 1.423244 | -1.096356 | 0.241921 | 1 | 0 |
| 20 | 0.572797 | 2.142771 | 1.581524 | 2.479349 | 0.473394 | -0.093008 | -0.084823 | -1.267718 | -1.284687 | -1.432013 | -1.251386 | -0.638749 | 0 | 0 |
| 21 | 0.862369 | 2.221963 | 1.673148 | -0.555264 | -0.741332 | -0.892991 | 0.221351 | -0.116798 | 0.378493 | 1.217874 | -1.085435 | -0.256045 | 1 | 0 |
| 22 | 1.498382 | -0.404560 | 0.195068 | -0.943467 | 0.085181 | -0.851680 | -0.552763 | 1.808182 | -0.441278 | 0.067722 | -0.458537 | 1.662711 | 1 | 0 |
| 23 | -1.364025 | 1.371234 | 1.673148 | -0.497512 | -0.157640 | -0.979363 | 0.415864 | -1.388894 | -1.148743 | 1.407283 | -1.247653 | -1.119587 | 1 | 0 |
| 24 | 1.498382 | -0.141656 | 1.241642 | 0.351671 | -0.814862 | -0.540976 | -0.626294 | -0.876743 | -0.616984 | -0.782913 | -0.866230 | 0.810379 | 1 | 0 |
| 25 | 1.498382 | -0.280382 | 1.532544 | 1.674414 | -0.727241 | -0.446776 | -0.604067 | 0.307172 | 0.222362 | -0.541751 | 0.720031 | 0.083232 | 0 | 0 |
| 26 | 1.075665 | -0.107428 | 1.673148 | 2.096608 | -0.226236 | 0.860102 | 0.163175 | 0.326457 | 0.203441 | -0.281441 | -0.236211 | 1.166053 | 0 | 0 |
| 27 | 0.540581 | 0.054204 | 0.505362 | -0.931144 | 0.436468 | -0.842085 | 0.277074 | 2.168789 | -0.135488 | 0.863810 | -0.521048 | 1.830173 | 1 | 0 |
| 28 | -0.980545 | -1.054168 | -1.263653 | -0.377459 | -1.133370 | -0.845439 | 0.110858 | -1.019562 | 0.100180 | -0.493484 | 0.325493 | 1.830174 | 1 | 0 |
| 29 | -0.441960 | 1.036991 | -0.878755 | 1.298082 | -0.666326 | 0.845380 | 1.113303 | -0.376141 | 1.439851 | -0.664701 | 1.224927 | 1.272102 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 156 | 0.964566 | -0.478675 | 0.600961 | -1.000674 | -0.743610 | -0.499950 | -0.696044 | -0.174243 | -0.686952 | 1.423244 | -1.030797 | -1.114829 | 1 | 1 |
| 157 | 0.270654 | 0.971091 | 0.132712 | 0.510175 | 0.258442 | 1.104875 | 0.136485 | 1.451829 | 1.439851 | -0.382326 | 0.865175 | -0.288952 | 0 | 1 |
| 158 | 0.388178 | -0.776533 | -1.121244 | -0.867688 | -0.827641 | -0.776813 | 0.973880 | -1.056551 | -0.596814 | 1.082405 | 0.005738 | 1.830173 | 1 | 1 |
| 159 | -0.409488 | -0.518845 | -0.902126 | -0.775313 | -0.624732 | -0.869850 | -0.153020 | -0.306394 | -0.007331 | 1.423244 | 0.011478 | 1.109037 | 1 | 1 |
| 160 | -1.188703 | -0.210659 | -0.626345 | -0.893545 | -0.918629 | -0.029765 | 2.569439 | -1.381276 | -1.323691 | -0.262947 | -1.017177 | 0.947571 | 1 | 1 |
| 161 | -1.476495 | -1.140106 | -1.266686 | -1.019916 | -1.162501 | 0.086770 | -0.874030 | -0.598831 | 1.439851 | -1.144185 | -1.294186 | -1.186653 | 1 | 1 |
| 162 | -1.075963 | -1.140106 | 0.768537 | -1.026562 | 2.565083 | 1.775966 | -0.950796 | -1.453844 | -1.349718 | 1.423244 | -0.660846 | -0.569048 | 1 | 1 |
| 163 | -1.142157 | -1.140106 | -0.917613 | -0.900746 | 2.578150 | -1.071687 | -0.937734 | -1.420511 | -1.345736 | 0.130051 | -1.058466 | -1.165382 | 1 | 1 |
| 164 | -0.756762 | -0.149815 | 1.673148 | 0.274589 | 0.009055 | 0.630144 | -0.689021 | 1.604745 | -1.053633 | 0.098821 | 0.463522 | -0.747055 | 1 | 1 |
| 165 | 0.429405 | -0.093236 | 1.263826 | 0.077597 | 0.114344 | -0.750943 | -0.299174 | 1.420985 | -0.848138 | 1.423244 | 0.247779 | 0.052884 | 1 | 1 |
| 166 | 0.553018 | -0.191462 | 1.673148 | -0.349285 | -0.196236 | -0.932266 | -0.717123 | -0.672655 | -1.120795 | -0.026811 | -0.537879 | -0.807588 | 1 | 1 |
| 167 | 0.997841 | 1.111726 | 1.673148 | 0.509323 | 1.012646 | -0.110920 | 1.607274 | 0.848930 | 0.336229 | 0.759005 | 0.460918 | 1.680913 | 0 | 1 |
| 168 | 1.281068 | 0.266174 | 0.446982 | 0.357357 | 0.348808 | -0.209890 | 0.845374 | 1.311409 | 0.971748 | 1.423244 | 0.410051 | 1.807237 | 0 | 1 |
| 169 | 0.013523 | -0.761110 | -1.031584 | -0.832016 | 0.088734 | -0.963108 | -0.407038 | -1.026311 | -0.384022 | 0.115756 | -0.062208 | 1.830174 | 1 | 1 |
| 170 | 0.666873 | -0.889641 | 1.409534 | 0.478248 | 0.640324 | -0.028408 | -0.137784 | -1.195061 | -1.343049 | -1.104665 | -0.345889 | 1.830173 | 1 | 1 |
| 171 | -0.179155 | -0.647105 | 1.673148 | 0.514384 | 0.469814 | -0.052937 | -0.435133 | -1.056954 | -1.372424 | -1.219127 | -0.571403 | 1.303219 | 1 | 1 |
| 172 | -0.698104 | -1.140106 | 1.673147 | 1.002618 | -1.030539 | 0.062021 | -0.950796 | -0.862502 | -1.410550 | -1.459509 | 0.007704 | -1.271527 | 1 | 1 |
| 173 | -0.221070 | -1.120261 | 0.098266 | -1.026027 | -1.172739 | -0.397041 | -0.950796 | 0.162128 | 0.351550 | 1.423244 | 0.924487 | -1.024174 | 1 | 1 |
| 174 | -0.987264 | -1.140106 | 1.430551 | 0.148205 | -1.172739 | -0.974773 | -0.950796 | 0.046605 | 0.349775 | 1.423244 | 1.009924 | -1.168197 | 1 | 1 |
| 175 | -0.001472 | 2.289616 | -0.469230 | -0.117523 | 0.087383 | -1.038762 | -0.386872 | -1.069931 | 0.726815 | 1.374128 | 0.589319 | 0.225042 | 0 | 1 |
| 176 | 0.759267 | 2.289616 | -0.807487 | -0.605891 | -0.207466 | -0.122817 | -0.928360 | -1.465473 | -0.403582 | 0.164132 | 0.091145 | 1.237470 | 0 | 1 |
| 177 | 0.303330 | 1.071917 | 0.410221 | 0.816208 | 0.386825 | -0.293228 | -0.059633 | 0.466962 | 1.121183 | 1.357331 | 1.812099 | 1.148304 | 0 | 1 |
| 178 | 0.371415 | 0.428878 | -0.437216 | 0.367689 | -0.713660 | 1.561886 | -0.345821 | -0.608625 | 0.845040 | 0.018040 | 1.812099 | -0.795570 | 0 | 1 |
| 179 | -1.106214 | 0.339351 | -0.739020 | -0.842920 | -0.830001 | -0.625489 | 0.083218 | 1.521028 | 1.439851 | 0.093070 | 1.538478 | -0.976780 | 0 | 1 |
| 180 | 0.970965 | 1.730559 | 1.300739 | 1.716251 | 2.578151 | 0.794509 | -0.890606 | -1.133358 | -1.410550 | -1.452853 | -1.269329 | -1.265541 | 0 | 1 |
| 181 | -1.092848 | -1.140019 | -1.014068 | -0.937922 | 2.397303 | 0.274619 | 0.231215 | 0.010249 | -0.572052 | 0.204326 | -1.044505 | 1.830173 | 1 | 1 |
| 182 | -0.432669 | -1.140106 | -1.275255 | -0.740960 | 2.578150 | -1.060066 | 1.058297 | -0.120367 | -1.278125 | -1.425308 | -1.294186 | 0.144247 | 1 | 1 |
| 183 | -1.222408 | -1.109984 | 0.115563 | -1.017839 | -0.845053 | -0.886348 | 0.708798 | 1.447822 | 0.072199 | 1.423244 | -0.469073 | 0.751450 | 1 | 1 |
| 184 | 1.498382 | 0.114899 | 0.573155 | -0.852483 | -0.059147 | 1.559801 | -0.088275 | 0.489614 | 0.106407 | 1.089262 | -0.583791 | 0.017900 | 0 | 1 |
| 185 | -0.252632 | -0.228432 | -0.156977 | 2.073566 | 1.013665 | 2.254737 | -0.135625 | -0.232305 | -0.538284 | -0.820780 | 0.474282 | -0.303243 | 0 | 1 |
186 rows × 14 columns
In [136]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[136]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ef63d8d7f0>
In [137]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[4]))
Specialized¶
ANN¶
In [463]:
X = df_n_ps_std_ch[4]
In [464]:
y = df_n_ps[4]['chosen']
In [465]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [466]:
X_train.shape
Out[466]:
(164, 12)
In [142]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [143]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [144]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [145]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20, 20), 'learning_rate_init': 0.001, 'max_iter': 500}, que permiten obtener un Accuracy de 64.63% y un Kappa del 27.16
Tiempo total: 22.23 minutos
In [467]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20,20]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr = 0.001
epochs = 500
In [468]:
input_tensor = Input(shape = (n0,))
In [469]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [470]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [471]:
model.summary()
Model: "model_25" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_25 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_79 (Dense) (None, 20) 260 _________________________________________________________________ dense_80 (Dense) (None, 20) 420 _________________________________________________________________ dense_81 (Dense) (None, 1) 21 ================================================================= Total params: 701 Trainable params: 701 Non-trainable params: 0 _________________________________________________________________
In [472]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 164 samples, validate on 55 samples Epoch 1/500 164/164 [==============================] - 0s 1ms/step - loss: 0.7345 - accuracy: 0.5488 - val_loss: 0.6907 - val_accuracy: 0.5455 Epoch 2/500 164/164 [==============================] - 0s 110us/step - loss: 0.7152 - accuracy: 0.5732 - val_loss: 0.6766 - val_accuracy: 0.5636 Epoch 3/500 164/164 [==============================] - 0s 79us/step - loss: 0.7022 - accuracy: 0.5610 - val_loss: 0.6723 - val_accuracy: 0.5636 Epoch 4/500 164/164 [==============================] - 0s 73us/step - loss: 0.6954 - accuracy: 0.5549 - val_loss: 0.6718 - val_accuracy: 0.5818 Epoch 5/500 164/164 [==============================] - 0s 85us/step - loss: 0.6887 - accuracy: 0.5549 - val_loss: 0.6672 - val_accuracy: 0.5818 Epoch 6/500 164/164 [==============================] - 0s 79us/step - loss: 0.6837 - accuracy: 0.5671 - val_loss: 0.6599 - val_accuracy: 0.6000 Epoch 7/500 164/164 [==============================] - 0s 79us/step - loss: 0.6786 - accuracy: 0.5732 - val_loss: 0.6541 - val_accuracy: 0.6000 Epoch 8/500 164/164 [==============================] - 0s 104us/step - loss: 0.6748 - accuracy: 0.5671 - val_loss: 0.6502 - val_accuracy: 0.6000 Epoch 9/500 164/164 [==============================] - 0s 104us/step - loss: 0.6722 - accuracy: 0.5610 - val_loss: 0.6461 - val_accuracy: 0.5818 Epoch 10/500 164/164 [==============================] - 0s 79us/step - loss: 0.6679 - accuracy: 0.5549 - val_loss: 0.6429 - val_accuracy: 0.6182 Epoch 11/500 164/164 [==============================] - 0s 67us/step - loss: 0.6651 - accuracy: 0.5610 - val_loss: 0.6387 - val_accuracy: 0.6364 Epoch 12/500 164/164 [==============================] - 0s 73us/step - loss: 0.6624 - accuracy: 0.5610 - val_loss: 0.6376 - val_accuracy: 0.6364 Epoch 13/500 164/164 [==============================] - 0s 73us/step - loss: 0.6591 - accuracy: 0.5732 - val_loss: 0.6371 - val_accuracy: 0.6364 Epoch 14/500 164/164 [==============================] - 0s 67us/step - loss: 0.6575 - accuracy: 0.5854 - val_loss: 0.6359 - val_accuracy: 0.6364 Epoch 15/500 164/164 [==============================] - 0s 73us/step - loss: 0.6553 - accuracy: 0.5976 - val_loss: 0.6344 - val_accuracy: 0.6545 Epoch 16/500 164/164 [==============================] - 0s 67us/step - loss: 0.6536 - accuracy: 0.5976 - val_loss: 0.6311 - val_accuracy: 0.6909 Epoch 17/500 164/164 [==============================] - 0s 67us/step - loss: 0.6519 - accuracy: 0.5915 - val_loss: 0.6286 - val_accuracy: 0.6727 Epoch 18/500 164/164 [==============================] - 0s 67us/step - loss: 0.6507 - accuracy: 0.6037 - val_loss: 0.6254 - val_accuracy: 0.6727 Epoch 19/500 164/164 [==============================] - 0s 85us/step - loss: 0.6491 - accuracy: 0.5976 - val_loss: 0.6217 - val_accuracy: 0.6909 Epoch 20/500 164/164 [==============================] - 0s 73us/step - loss: 0.6474 - accuracy: 0.5976 - val_loss: 0.6230 - val_accuracy: 0.6727 Epoch 21/500 164/164 [==============================] - 0s 73us/step - loss: 0.6458 - accuracy: 0.6098 - val_loss: 0.6213 - val_accuracy: 0.6727 Epoch 22/500 164/164 [==============================] - 0s 73us/step - loss: 0.6445 - accuracy: 0.6098 - val_loss: 0.6206 - val_accuracy: 0.6909 Epoch 23/500 164/164 [==============================] - 0s 79us/step - loss: 0.6431 - accuracy: 0.6098 - val_loss: 0.6210 - val_accuracy: 0.6909 Epoch 24/500 164/164 [==============================] - 0s 67us/step - loss: 0.6418 - accuracy: 0.6159 - val_loss: 0.6209 - val_accuracy: 0.6909 Epoch 25/500 164/164 [==============================] - 0s 67us/step - loss: 0.6406 - accuracy: 0.6159 - val_loss: 0.6205 - val_accuracy: 0.6909 Epoch 26/500 164/164 [==============================] - 0s 85us/step - loss: 0.6394 - accuracy: 0.6159 - val_loss: 0.6213 - val_accuracy: 0.6727 Epoch 00026: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 27/500 164/164 [==============================] - 0s 79us/step - loss: 0.6382 - accuracy: 0.6098 - val_loss: 0.6231 - val_accuracy: 0.6909 Epoch 28/500 164/164 [==============================] - 0s 67us/step - loss: 0.6375 - accuracy: 0.6098 - val_loss: 0.6234 - val_accuracy: 0.6909 Epoch 29/500 164/164 [==============================] - 0s 61us/step - loss: 0.6367 - accuracy: 0.6098 - val_loss: 0.6217 - val_accuracy: 0.6727 Epoch 30/500 164/164 [==============================] - 0s 73us/step - loss: 0.6359 - accuracy: 0.6159 - val_loss: 0.6199 - val_accuracy: 0.6727 Epoch 31/500 164/164 [==============================] - 0s 67us/step - loss: 0.6352 - accuracy: 0.6159 - val_loss: 0.6197 - val_accuracy: 0.6727 Epoch 32/500 164/164 [==============================] - 0s 85us/step - loss: 0.6344 - accuracy: 0.6159 - val_loss: 0.6200 - val_accuracy: 0.6727 Epoch 33/500 164/164 [==============================] - 0s 73us/step - loss: 0.6334 - accuracy: 0.6220 - val_loss: 0.6209 - val_accuracy: 0.6545 Epoch 34/500 164/164 [==============================] - 0s 67us/step - loss: 0.6325 - accuracy: 0.6220 - val_loss: 0.6205 - val_accuracy: 0.6727 Epoch 35/500 164/164 [==============================] - 0s 79us/step - loss: 0.6318 - accuracy: 0.6159 - val_loss: 0.6181 - val_accuracy: 0.6727 Epoch 36/500 164/164 [==============================] - 0s 158us/step - loss: 0.6310 - accuracy: 0.6098 - val_loss: 0.6161 - val_accuracy: 0.6909 Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 37/500 164/164 [==============================] - 0s 91us/step - loss: 0.6306 - accuracy: 0.6220 - val_loss: 0.6150 - val_accuracy: 0.6909 Epoch 38/500 164/164 [==============================] - 0s 91us/step - loss: 0.6300 - accuracy: 0.6220 - val_loss: 0.6148 - val_accuracy: 0.6909 Epoch 39/500 164/164 [==============================] - 0s 73us/step - loss: 0.6298 - accuracy: 0.6220 - val_loss: 0.6149 - val_accuracy: 0.6909 Epoch 40/500 164/164 [==============================] - 0s 67us/step - loss: 0.6294 - accuracy: 0.6220 - val_loss: 0.6148 - val_accuracy: 0.6909 Epoch 41/500 164/164 [==============================] - 0s 67us/step - loss: 0.6289 - accuracy: 0.6220 - val_loss: 0.6157 - val_accuracy: 0.6727 Epoch 42/500 164/164 [==============================] - 0s 67us/step - loss: 0.6285 - accuracy: 0.6159 - val_loss: 0.6164 - val_accuracy: 0.6727 Epoch 43/500 164/164 [==============================] - 0s 67us/step - loss: 0.6281 - accuracy: 0.6098 - val_loss: 0.6173 - val_accuracy: 0.6727 Epoch 44/500 164/164 [==============================] - 0s 67us/step - loss: 0.6277 - accuracy: 0.6098 - val_loss: 0.6177 - val_accuracy: 0.6727 Epoch 45/500 164/164 [==============================] - 0s 79us/step - loss: 0.6275 - accuracy: 0.6037 - val_loss: 0.6184 - val_accuracy: 0.6727 Epoch 46/500 164/164 [==============================] - 0s 73us/step - loss: 0.6271 - accuracy: 0.6037 - val_loss: 0.6191 - val_accuracy: 0.6727 Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 47/500 164/164 [==============================] - 0s 73us/step - loss: 0.6268 - accuracy: 0.6037 - val_loss: 0.6190 - val_accuracy: 0.6727 Epoch 48/500 164/164 [==============================] - 0s 67us/step - loss: 0.6267 - accuracy: 0.6098 - val_loss: 0.6184 - val_accuracy: 0.6727 Epoch 49/500 164/164 [==============================] - 0s 73us/step - loss: 0.6265 - accuracy: 0.6159 - val_loss: 0.6185 - val_accuracy: 0.6727 Epoch 50/500 164/164 [==============================] - 0s 67us/step - loss: 0.6264 - accuracy: 0.6159 - val_loss: 0.6182 - val_accuracy: 0.6727 Epoch 51/500 164/164 [==============================] - 0s 67us/step - loss: 0.6262 - accuracy: 0.6159 - val_loss: 0.6180 - val_accuracy: 0.6727 Epoch 52/500 164/164 [==============================] - 0s 73us/step - loss: 0.6259 - accuracy: 0.6159 - val_loss: 0.6182 - val_accuracy: 0.6727 Epoch 53/500 164/164 [==============================] - 0s 67us/step - loss: 0.6257 - accuracy: 0.6159 - val_loss: 0.6186 - val_accuracy: 0.6727 Epoch 54/500 164/164 [==============================] - 0s 67us/step - loss: 0.6256 - accuracy: 0.6159 - val_loss: 0.6191 - val_accuracy: 0.6727 Epoch 55/500 164/164 [==============================] - 0s 61us/step - loss: 0.6253 - accuracy: 0.6159 - val_loss: 0.6192 - val_accuracy: 0.6727 Epoch 56/500 164/164 [==============================] - 0s 61us/step - loss: 0.6251 - accuracy: 0.6159 - val_loss: 0.6195 - val_accuracy: 0.6364 Epoch 00056: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 57/500 164/164 [==============================] - 0s 73us/step - loss: 0.6249 - accuracy: 0.6098 - val_loss: 0.6194 - val_accuracy: 0.6364 Epoch 58/500 164/164 [==============================] - 0s 85us/step - loss: 0.6249 - accuracy: 0.6098 - val_loss: 0.6195 - val_accuracy: 0.6545 Epoch 59/500 164/164 [==============================] - 0s 116us/step - loss: 0.6247 - accuracy: 0.6098 - val_loss: 0.6194 - val_accuracy: 0.6545 Epoch 60/500 164/164 [==============================] - 0s 85us/step - loss: 0.6246 - accuracy: 0.6098 - val_loss: 0.6194 - val_accuracy: 0.6545 Epoch 61/500 164/164 [==============================] - 0s 79us/step - loss: 0.6245 - accuracy: 0.6098 - val_loss: 0.6194 - val_accuracy: 0.6545 Epoch 62/500 164/164 [==============================] - 0s 73us/step - loss: 0.6244 - accuracy: 0.6159 - val_loss: 0.6193 - val_accuracy: 0.6545 Epoch 63/500 164/164 [==============================] - 0s 79us/step - loss: 0.6243 - accuracy: 0.6220 - val_loss: 0.6192 - val_accuracy: 0.6545 Epoch 64/500 164/164 [==============================] - 0s 73us/step - loss: 0.6242 - accuracy: 0.6220 - val_loss: 0.6193 - val_accuracy: 0.6545 Epoch 65/500 164/164 [==============================] - 0s 73us/step - loss: 0.6241 - accuracy: 0.6220 - val_loss: 0.6194 - val_accuracy: 0.6545 Epoch 66/500 164/164 [==============================] - 0s 73us/step - loss: 0.6240 - accuracy: 0.6220 - val_loss: 0.6196 - val_accuracy: 0.6545 Epoch 00066: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 67/500 164/164 [==============================] - 0s 67us/step - loss: 0.6239 - accuracy: 0.6220 - val_loss: 0.6196 - val_accuracy: 0.6545 Epoch 68/500 164/164 [==============================] - 0s 67us/step - loss: 0.6238 - accuracy: 0.6220 - val_loss: 0.6197 - val_accuracy: 0.6545 Epoch 69/500 164/164 [==============================] - 0s 79us/step - loss: 0.6238 - accuracy: 0.6280 - val_loss: 0.6197 - val_accuracy: 0.6545 Epoch 70/500 164/164 [==============================] - 0s 67us/step - loss: 0.6238 - accuracy: 0.6280 - val_loss: 0.6197 - val_accuracy: 0.6545 Epoch 71/500 164/164 [==============================] - 0s 79us/step - loss: 0.6237 - accuracy: 0.6280 - val_loss: 0.6198 - val_accuracy: 0.6545 Epoch 72/500 164/164 [==============================] - 0s 73us/step - loss: 0.6237 - accuracy: 0.6280 - val_loss: 0.6198 - val_accuracy: 0.6545 Epoch 73/500 164/164 [==============================] - 0s 73us/step - loss: 0.6236 - accuracy: 0.6280 - val_loss: 0.6198 - val_accuracy: 0.6545 Epoch 74/500 164/164 [==============================] - 0s 98us/step - loss: 0.6235 - accuracy: 0.6280 - val_loss: 0.6198 - val_accuracy: 0.6545 Epoch 75/500 164/164 [==============================] - 0s 73us/step - loss: 0.6235 - accuracy: 0.6280 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 76/500 164/164 [==============================] - 0s 67us/step - loss: 0.6234 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 00076: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 77/500 164/164 [==============================] - 0s 67us/step - loss: 0.6234 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 78/500 164/164 [==============================] - 0s 67us/step - loss: 0.6234 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 79/500 164/164 [==============================] - 0s 67us/step - loss: 0.6233 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 80/500 164/164 [==============================] - 0s 61us/step - loss: 0.6233 - accuracy: 0.6341 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 81/500 164/164 [==============================] - 0s 61us/step - loss: 0.6233 - accuracy: 0.6341 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 82/500 164/164 [==============================] - 0s 67us/step - loss: 0.6233 - accuracy: 0.6341 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 83/500 164/164 [==============================] - 0s 67us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 84/500 164/164 [==============================] - 0s 67us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 85/500 164/164 [==============================] - 0s 110us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 86/500 164/164 [==============================] - 0s 79us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 00086: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06. Epoch 87/500 164/164 [==============================] - 0s 73us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 88/500 164/164 [==============================] - 0s 67us/step - loss: 0.6232 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 89/500 164/164 [==============================] - 0s 67us/step - loss: 0.6231 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 90/500 164/164 [==============================] - 0s 73us/step - loss: 0.6231 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 91/500 164/164 [==============================] - 0s 98us/step - loss: 0.6231 - accuracy: 0.6341 - val_loss: 0.6199 - val_accuracy: 0.6545 Epoch 92/500 164/164 [==============================] - 0s 73us/step - loss: 0.6231 - accuracy: 0.6280 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 93/500 164/164 [==============================] - 0s 73us/step - loss: 0.6231 - accuracy: 0.6280 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 94/500 164/164 [==============================] - 0s 67us/step - loss: 0.6231 - accuracy: 0.6280 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 95/500 164/164 [==============================] - 0s 85us/step - loss: 0.6231 - accuracy: 0.6280 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 96/500 164/164 [==============================] - 0s 73us/step - loss: 0.6231 - accuracy: 0.6280 - val_loss: 0.6200 - val_accuracy: 0.6545 Epoch 00096: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06. Epoch 97/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6280 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 98/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6280 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 99/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 100/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 101/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 102/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 103/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 104/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 105/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 106/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00106: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06. Epoch 107/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 108/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 109/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 110/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 111/500 164/164 [==============================] - 0s 91us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 112/500 164/164 [==============================] - 0s 85us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 113/500 164/164 [==============================] - 0s 85us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 114/500 164/164 [==============================] - 0s 67us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 115/500 164/164 [==============================] - 0s 85us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 116/500 164/164 [==============================] - 0s 91us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 00116: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07. Epoch 117/500 164/164 [==============================] - 0s 91us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 118/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 119/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 120/500 164/164 [==============================] - 0s 73us/step - loss: 0.6230 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 121/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 122/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 123/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 124/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 125/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 126/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00126: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07. Epoch 127/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 128/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 129/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 130/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 131/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 132/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 133/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 134/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 135/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 136/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 00136: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07. Epoch 137/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6201 - val_accuracy: 0.6545 Epoch 138/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 139/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 140/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 141/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 142/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 143/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 144/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 145/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 146/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00146: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07. Epoch 147/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 148/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 149/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 150/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 151/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 152/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 153/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 154/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 155/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 156/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00156: ReduceLROnPlateau reducing learning rate to 6.103515914901436e-08. Epoch 157/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 158/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 159/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 160/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 161/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 162/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 163/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 164/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 165/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 166/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00166: ReduceLROnPlateau reducing learning rate to 3.051757957450718e-08. Epoch 167/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 168/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 169/500 164/164 [==============================] - 0s 116us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 170/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 171/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 172/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 173/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 174/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 175/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 176/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00176: ReduceLROnPlateau reducing learning rate to 1.525878978725359e-08. Epoch 177/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 178/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 179/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 180/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 181/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 182/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 183/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 184/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 185/500 164/164 [==============================] - 0s 128us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 186/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00186: ReduceLROnPlateau reducing learning rate to 7.629394893626795e-09. Epoch 187/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 188/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 189/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 190/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 191/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 192/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 193/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 194/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 195/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 196/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00196: ReduceLROnPlateau reducing learning rate to 3.814697446813398e-09. Epoch 197/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 198/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 199/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 200/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 201/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 202/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 203/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 204/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 205/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 206/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00206: ReduceLROnPlateau reducing learning rate to 1.907348723406699e-09. Epoch 207/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 208/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 209/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 210/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 211/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 212/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 213/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 214/500 164/164 [==============================] - 0s 97us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 215/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 216/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00216: ReduceLROnPlateau reducing learning rate to 9.536743617033494e-10. Epoch 217/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 218/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 219/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 220/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 221/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 222/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 223/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 224/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 225/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 226/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00226: ReduceLROnPlateau reducing learning rate to 4.768371808516747e-10. Epoch 227/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 228/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 229/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 230/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 231/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 232/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 233/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 234/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 235/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 236/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00236: ReduceLROnPlateau reducing learning rate to 2.3841859042583735e-10. Epoch 237/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 238/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 239/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 240/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 241/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 242/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 243/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 244/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 245/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 246/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00246: ReduceLROnPlateau reducing learning rate to 1.1920929521291868e-10. Epoch 247/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 248/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 249/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 250/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 251/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 252/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 253/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 254/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 255/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 256/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00256: ReduceLROnPlateau reducing learning rate to 5.960464760645934e-11. Epoch 257/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 258/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 259/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 260/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 261/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 262/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 263/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 264/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 265/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 266/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00266: ReduceLROnPlateau reducing learning rate to 2.980232380322967e-11. Epoch 267/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 268/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 269/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 270/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 271/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 272/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 273/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 274/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 275/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 276/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00276: ReduceLROnPlateau reducing learning rate to 1.4901161901614834e-11. Epoch 277/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 278/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 279/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 280/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 281/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 282/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 283/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 284/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 285/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 286/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00286: ReduceLROnPlateau reducing learning rate to 7.450580950807417e-12. Epoch 287/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 288/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 289/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 290/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 291/500 164/164 [==============================] - ETA: 0s - loss: 0.6214 - accuracy: 0.68 - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 292/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 293/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 294/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 295/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 296/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00296: ReduceLROnPlateau reducing learning rate to 3.725290475403709e-12. Epoch 297/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 298/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 299/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 300/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 301/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 302/500 164/164 [==============================] - 0s 104us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 303/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 304/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 305/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 306/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00306: ReduceLROnPlateau reducing learning rate to 1.8626452377018543e-12. Epoch 307/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 308/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 309/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 310/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 311/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 312/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 313/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 314/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 315/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 316/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00316: ReduceLROnPlateau reducing learning rate to 9.313226188509272e-13. Epoch 317/500 164/164 [==============================] - 0s 110us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 318/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 319/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 320/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 321/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 322/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 323/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 324/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 325/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 326/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00326: ReduceLROnPlateau reducing learning rate to 4.656613094254636e-13. Epoch 327/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 328/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 329/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 330/500 164/164 [==============================] - 0s 104us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 331/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 332/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 333/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 334/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 335/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 336/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00336: ReduceLROnPlateau reducing learning rate to 2.328306547127318e-13. Epoch 337/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 338/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 339/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 340/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 341/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 342/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 343/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 344/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 345/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 346/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00346: ReduceLROnPlateau reducing learning rate to 1.164153273563659e-13. Epoch 347/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 348/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 349/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 350/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 351/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 352/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 353/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 354/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 355/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 356/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00356: ReduceLROnPlateau reducing learning rate to 5.820766367818295e-14. Epoch 357/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 358/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 359/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 360/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 361/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 362/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 363/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 364/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 365/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 366/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00366: ReduceLROnPlateau reducing learning rate to 2.9103831839091474e-14. Epoch 367/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 368/500 164/164 [==============================] - 0s 104us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 369/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 370/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 371/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 372/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 373/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 374/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 375/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 376/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00376: ReduceLROnPlateau reducing learning rate to 1.4551915919545737e-14. Epoch 377/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 378/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 379/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 380/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 381/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 382/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 383/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 384/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 385/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 386/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00386: ReduceLROnPlateau reducing learning rate to 7.275957959772868e-15. Epoch 387/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 388/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 389/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 390/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 391/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 392/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 393/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 394/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 395/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 396/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00396: ReduceLROnPlateau reducing learning rate to 3.637978979886434e-15. Epoch 397/500 164/164 [==============================] - 0s 122us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 398/500 164/164 [==============================] - 0s 97us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 399/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 400/500 164/164 [==============================] - 0s 140us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 401/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 402/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 403/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 404/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 405/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 406/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00406: ReduceLROnPlateau reducing learning rate to 1.818989489943217e-15. Epoch 407/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 408/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 409/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 410/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 411/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 412/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 413/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 414/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 415/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 416/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00416: ReduceLROnPlateau reducing learning rate to 9.094947449716085e-16. Epoch 417/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 418/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 419/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 420/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 421/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 422/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 423/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 424/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 425/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 426/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00426: ReduceLROnPlateau reducing learning rate to 4.547473724858043e-16. Epoch 427/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 428/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 429/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 430/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 431/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 432/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 433/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 434/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 435/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 436/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00436: ReduceLROnPlateau reducing learning rate to 2.2737368624290214e-16. Epoch 437/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 438/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 439/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 440/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 441/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 442/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 443/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 444/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 445/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 446/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00446: ReduceLROnPlateau reducing learning rate to 1.1368684312145107e-16. Epoch 447/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 448/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 449/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 450/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 451/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 452/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 453/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 454/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 455/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 456/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00456: ReduceLROnPlateau reducing learning rate to 5.684342156072553e-17. Epoch 457/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 458/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 459/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 460/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 461/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 462/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 463/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 464/500 164/164 [==============================] - 0s 79us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 465/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 466/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00466: ReduceLROnPlateau reducing learning rate to 2.842171078036277e-17. Epoch 467/500 164/164 [==============================] - 0s 98us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 468/500 164/164 [==============================] - 0s 85us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 469/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 470/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 471/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 472/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 473/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 474/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 475/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 476/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00476: ReduceLROnPlateau reducing learning rate to 1.4210855390181384e-17. Epoch 477/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 478/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 479/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 480/500 164/164 [==============================] - 0s 91us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 481/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 482/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 483/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 484/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 485/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 486/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00486: ReduceLROnPlateau reducing learning rate to 7.105427695090692e-18. Epoch 487/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 488/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 489/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 490/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 491/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 492/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 493/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 494/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 495/500 164/164 [==============================] - 0s 67us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 496/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 00496: ReduceLROnPlateau reducing learning rate to 3.552713847545346e-18. Epoch 497/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 498/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 499/500 164/164 [==============================] - 0s 73us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545 Epoch 500/500 164/164 [==============================] - 0s 61us/step - loss: 0.6229 - accuracy: 0.6341 - val_loss: 0.6202 - val_accuracy: 0.6545
In [473]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 500)
In [474]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
55/55 [==============================] - 0s 55us/step test loss: 0.6201585899699819, test accuracy: 0.6545454263687134
In [475]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.7365591397849462
In [476]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.2943956785955435
KMeans¶
In [156]:
X
Out[156]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.752761 | -1.114598 | -0.348132 | 2.966304 | -0.925235 | 0.552064 | -0.710296 | 0.412009 | -0.784684 | -1.667162 | -0.834151 | -1.566379 |
| 1 | -1.387006 | -0.333039 | 0.041297 | -0.917052 | 0.656635 | -1.022407 | -0.805166 | -0.905135 | -0.708805 | 1.299875 | -0.948816 | -1.413196 |
| 2 | -0.628834 | 2.234144 | 0.613536 | -0.978644 | 0.836157 | -0.735689 | -0.059767 | -1.571350 | 1.278264 | -1.103616 | -1.153426 | 0.685062 |
| 3 | 0.081693 | 1.765530 | -0.365668 | 0.759057 | -1.136519 | -0.071939 | -0.412587 | -1.310708 | 1.465231 | -1.266573 | 0.040076 | -0.308065 |
| 4 | 0.056206 | 1.646501 | 0.508800 | 0.525847 | 0.506842 | -0.390517 | -0.241209 | -0.409725 | 1.465231 | -0.148455 | -0.354779 | 0.188297 |
| 5 | -0.475284 | 0.673200 | -1.077774 | 0.360339 | -0.032017 | 0.910768 | 1.405699 | -0.748908 | 1.465231 | -0.933838 | -0.225835 | -0.922170 |
| 6 | 0.163433 | -0.011304 | -1.057752 | 1.128932 | 0.026597 | 1.324397 | -0.060004 | -0.869144 | 1.465231 | -0.902156 | 0.598522 | -1.044263 |
| 7 | 0.110500 | 0.178211 | -1.394026 | 0.897710 | -1.243991 | -0.384906 | -0.976009 | -1.391712 | 1.465231 | -1.735560 | -0.662454 | -1.567335 |
| 8 | 0.595793 | -0.898440 | 1.924099 | 0.051119 | 0.528166 | 0.377574 | -0.881206 | 1.992593 | -0.169332 | 0.289379 | -1.264549 | -0.986278 |
| 9 | -0.017542 | -1.386211 | 0.574605 | -1.268664 | -0.911663 | -1.241512 | -1.036865 | 1.992592 | -1.595580 | 0.436331 | -1.528923 | -1.224807 |
| 10 | 0.796579 | -1.216664 | -0.112327 | -0.690935 | 1.077368 | 0.874900 | -0.528379 | 1.992593 | -0.347105 | 1.295264 | 0.069115 | -0.797501 |
| 11 | 1.761253 | -0.949116 | -0.297777 | -0.913826 | -0.875567 | -0.968315 | -0.407542 | -0.016419 | -0.556271 | 0.391283 | 0.743737 | 0.460341 |
| 12 | 1.055147 | -0.394702 | 1.258031 | -0.517345 | -0.021328 | -0.557321 | 0.669125 | 1.243976 | 0.262937 | 0.337908 | 0.485227 | 2.007205 |
| 13 | 1.761253 | -0.435392 | 0.592085 | -0.692391 | 0.535758 | -0.708164 | -0.382176 | 0.125232 | -0.083947 | 0.888176 | 0.994199 | 1.087612 |
| 14 | -0.137413 | -0.980041 | -1.297302 | -0.880795 | 1.395884 | -0.901503 | -0.756382 | -0.304071 | -1.277311 | 1.299875 | -1.162641 | -1.274290 |
| 15 | 0.017749 | -0.936126 | -1.136240 | -0.862914 | 0.782285 | -0.230220 | -0.734923 | -0.295573 | -1.221528 | 1.299875 | -1.058979 | -1.237947 |
| 16 | -0.382429 | -1.386211 | -1.437693 | -1.268664 | 0.481905 | -1.219324 | -1.036865 | 0.284908 | -1.749408 | 1.299875 | -1.532659 | -1.596787 |
| 17 | -1.227998 | 1.294887 | 2.200026 | -0.318659 | 2.364886 | -1.078770 | -0.029818 | -1.427905 | -1.667310 | 0.632080 | -1.532496 | 0.393871 |
| 18 | -1.076569 | -0.776099 | -0.576736 | -0.929915 | 0.188222 | -1.067980 | 0.156056 | -0.671783 | -0.806376 | 1.299875 | -0.994450 | -0.887100 |
| 19 | -1.180746 | 0.385053 | 2.200026 | -0.005979 | 1.302093 | -0.928084 | -0.007355 | -0.793418 | -1.747571 | -0.917240 | -1.528514 | -0.042363 |
| 20 | 1.192498 | -0.224351 | -0.206238 | 0.217971 | 0.304072 | 0.949289 | -0.514273 | 1.091683 | 1.465231 | -0.718685 | -0.298094 | -0.264379 |
| 21 | 1.213472 | 0.048184 | -0.600330 | 0.007879 | 0.177356 | 1.215438 | -0.394095 | 0.833193 | 1.465231 | -0.381808 | -0.415137 | -0.479222 |
| 22 | -0.004572 | -0.052026 | 2.200026 | -0.008377 | 1.967963 | -0.419263 | -0.302302 | 0.046448 | -0.343181 | 1.114245 | -0.245566 | 0.544195 |
| 23 | -0.229893 | 0.630662 | 0.484895 | 0.048344 | -0.498767 | 0.662733 | -0.509073 | 0.013777 | 1.465231 | 0.438954 | -0.681106 | -0.745425 |
| 24 | 0.217943 | -0.177066 | 0.659322 | -0.134414 | 1.650468 | -0.583176 | -0.559211 | -0.406584 | -0.598204 | 1.299875 | -0.406852 | 0.234437 |
| 25 | -0.333597 | 0.972324 | -0.721724 | -0.554449 | -0.493410 | 0.518707 | 0.321268 | -1.070955 | -0.564510 | -0.833869 | 1.695138 | -0.783758 |
| 26 | -0.602430 | -0.028610 | -0.826715 | -0.377974 | -0.696055 | -0.182813 | -0.120202 | -0.886178 | -0.584506 | -0.673011 | 1.695138 | -0.599349 |
| 27 | -0.251812 | -0.171571 | -0.358821 | -0.244636 | -0.013843 | 0.945000 | 1.038022 | -0.727984 | 0.773167 | -0.048067 | 1.695138 | 0.563056 |
| 28 | -0.368678 | 2.234146 | -0.330756 | -0.138877 | -0.559553 | -0.851160 | -0.744847 | -0.884446 | -0.828451 | -1.011890 | -0.568588 | 0.130393 |
| 29 | -0.578621 | 2.234146 | -0.562376 | -0.105376 | -0.523903 | -0.825786 | -0.690314 | -0.809436 | -0.870132 | -1.142574 | -0.536734 | 0.859716 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.186741 | -1.165084 | -0.921181 | 1.397816 | -0.045251 | 1.304725 | -0.153535 | 1.038843 | 1.465231 | 0.175798 | 1.209572 | -0.558994 |
| 190 | -0.163966 | 0.884601 | 1.657605 | -0.260803 | 1.379747 | -0.732537 | -0.007962 | 1.276498 | -0.419568 | 1.299875 | -0.588765 | 1.807669 |
| 191 | -0.254097 | -0.617571 | 0.535718 | -0.297186 | 1.865594 | -0.310035 | 0.916200 | 1.349280 | -0.168599 | 1.299875 | -0.479982 | 1.556484 |
| 192 | -1.359431 | 0.228998 | 0.259367 | -1.169606 | 0.516753 | -1.080823 | 1.794240 | -0.257346 | -1.633246 | 0.226711 | -1.071882 | 2.007205 |
| 193 | 1.622617 | 2.234146 | -1.127605 | -0.273622 | -1.141311 | 0.831169 | -0.681956 | -1.023930 | 0.097381 | -1.505036 | -0.432936 | -0.703132 |
| 194 | -0.467818 | -0.314482 | 0.014154 | 0.025208 | -0.807816 | -0.093075 | 3.055878 | 0.013994 | -0.514838 | -0.698969 | 0.430445 | -0.302544 |
| 195 | -1.423661 | -0.599038 | -0.707969 | -1.084898 | -1.206212 | -0.474886 | 1.273960 | -1.605019 | -1.163780 | -1.777082 | 1.695138 | -1.000177 |
| 196 | 1.761253 | -0.325612 | 0.357954 | -0.709817 | 1.043699 | -0.927401 | -0.653399 | -0.107407 | -0.553233 | -0.078311 | -0.687092 | -0.080809 |
| 197 | 1.761253 | 1.028574 | 1.374947 | 0.116683 | 1.136600 | -0.187955 | -0.195137 | 0.341797 | -0.856237 | 0.366485 | -0.658035 | 0.645642 |
| 198 | 1.761253 | 0.050043 | 0.687757 | -0.434894 | 0.705291 | -0.680095 | -0.549325 | 0.816661 | -0.567882 | 0.584770 | -0.302600 | -0.144573 |
| 199 | -0.919189 | -0.200271 | -1.032880 | -0.020720 | -1.069924 | -0.649964 | -0.843737 | -0.600356 | 1.465231 | -0.271885 | 0.807204 | -0.901281 |
| 200 | -0.988568 | -0.189392 | -1.028379 | 0.102199 | -1.085401 | -0.667213 | -0.580786 | -0.498427 | 1.465231 | -0.287173 | 0.737684 | -0.938325 |
| 201 | -0.779270 | 0.427427 | -0.948100 | -0.165441 | -1.033638 | 0.272258 | -0.022274 | -0.198353 | 1.465231 | -0.072695 | 1.561935 | -0.848980 |
| 202 | 1.116127 | -1.139042 | 0.168460 | -1.122851 | -0.854043 | 1.067951 | -0.735589 | 0.656822 | 0.343006 | 1.299875 | 0.776152 | -0.247153 |
| 203 | 0.762440 | -1.190866 | -0.050517 | -1.116856 | -1.095215 | 0.632777 | -0.797158 | 1.167398 | 1.149430 | 1.299875 | 1.392402 | 0.047424 |
| 204 | 0.058411 | -1.128496 | -1.328850 | -1.229911 | -1.074617 | -0.085788 | -0.812628 | 0.326031 | 0.625580 | 1.299875 | 1.290873 | -0.077297 |
| 205 | -0.720270 | -0.697672 | -0.720394 | 1.813110 | -0.859595 | -0.861783 | -0.908744 | 1.216314 | 1.411592 | -0.405169 | 1.695138 | -0.439681 |
| 206 | -0.364461 | -0.010160 | -1.095500 | 0.914425 | -1.040229 | 0.885108 | -0.998962 | 0.772555 | 1.465231 | -0.838613 | 0.940082 | -0.955178 |
| 207 | -0.272236 | -0.196860 | -0.727982 | 0.141418 | -0.904008 | -0.065613 | -0.944991 | 0.795648 | 0.611803 | 0.903563 | 1.695137 | -0.158991 |
| 208 | 1.761253 | -0.082523 | -0.122308 | 0.265346 | -0.485445 | 0.500219 | 0.126422 | -0.354469 | 0.139245 | -0.560750 | 1.149004 | 0.354466 |
| 209 | 1.573516 | -0.161473 | -0.359909 | 1.241760 | -0.664508 | 1.002463 | -0.076931 | 0.519366 | 1.465231 | -0.646843 | 0.532185 | -0.215564 |
| 210 | 1.761253 | 0.417875 | -0.918851 | -0.929813 | -0.982357 | -0.771036 | -0.949451 | -0.679224 | -0.652751 | -1.604058 | -0.786724 | -0.848956 |
| 211 | 1.761253 | 0.145550 | 1.710575 | 1.914520 | 1.233461 | 2.474950 | 0.953725 | 1.263177 | 0.830563 | 0.011243 | 0.602765 | -0.024981 |
| 212 | 0.470346 | -0.334996 | 2.200026 | 1.858653 | 0.847856 | 1.364055 | 0.061293 | 1.366431 | -0.301856 | -0.501569 | -0.235203 | -0.695720 |
| 213 | -0.576457 | -0.914445 | 1.070087 | 0.337357 | 0.306857 | 0.394672 | -0.372356 | -0.450091 | -0.240434 | 0.165141 | 1.695138 | 0.345150 |
| 214 | 1.761253 | -0.591066 | -0.690824 | 2.065965 | -0.721336 | -0.340791 | -0.483151 | 0.855908 | 0.529996 | -1.116013 | 0.527710 | -0.050391 |
| 215 | 1.490806 | -1.368871 | -1.151960 | 2.846487 | -0.924825 | 0.052478 | -0.970103 | 1.992593 | 0.913611 | -0.452243 | 0.613814 | -1.529552 |
| 216 | 0.191801 | -1.348512 | -1.315236 | -0.455163 | -1.244101 | -1.240530 | -1.036865 | -0.531344 | 1.465231 | -1.809822 | -1.350349 | -1.513618 |
| 217 | -0.002098 | 2.039653 | -0.752917 | 0.971355 | -0.795869 | 0.431147 | -0.753214 | 0.043687 | 1.465231 | -1.105865 | -0.938582 | -0.984328 |
| 218 | -0.098688 | -0.923087 | -0.917548 | 0.312310 | -0.183969 | 0.248120 | -0.545773 | 0.584070 | 0.733937 | -0.697562 | 1.695138 | -0.497086 |
219 rows × 12 columns
In [157]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[157]:
[2628.0, 2183.3505259693475, 1946.7709026205612, 1764.2381744586387, 1667.52081260375, 1588.695626080069, 1521.6546082793252, 1465.3583612489235, 1381.9950671234758, 1338.0035802619554, 1275.054416118868, 1244.4334212962274, 1201.2393010186188, 1166.7934845697623]
In [158]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[158]:
[<matplotlib.lines.Line2D at 0x1ef5e4c4748>]
K=2
In [159]:
kmeans_ch = KMeans(n_clusters=2, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[159]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [160]:
kmeans_ch.labels_
Out[160]:
array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In [161]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[161]:
array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In [162]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [163]:
X
Out[163]:
| chromagramfiles_1 | chromagramfiles_2 | chromagramfiles_3 | chromagramfiles_4 | chromagramfiles_5 | chromagramfiles_6 | chromagramfiles_7 | chromagramfiles_8 | chromagramfiles_9 | chromagramfiles_10 | chromagramfiles_11 | chromagramfiles_12 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.752761 | -1.114598 | -0.348132 | 2.966304 | -0.925235 | 0.552064 | -0.710296 | 0.412009 | -0.784684 | -1.667162 | -0.834151 | -1.566379 | 0 | 0 |
| 1 | -1.387006 | -0.333039 | 0.041297 | -0.917052 | 0.656635 | -1.022407 | -0.805166 | -0.905135 | -0.708805 | 1.299875 | -0.948816 | -1.413196 | 1 | 0 |
| 2 | -0.628834 | 2.234144 | 0.613536 | -0.978644 | 0.836157 | -0.735689 | -0.059767 | -1.571350 | 1.278264 | -1.103616 | -1.153426 | 0.685062 | 1 | 0 |
| 3 | 0.081693 | 1.765530 | -0.365668 | 0.759057 | -1.136519 | -0.071939 | -0.412587 | -1.310708 | 1.465231 | -1.266573 | 0.040076 | -0.308065 | 0 | 0 |
| 4 | 0.056206 | 1.646501 | 0.508800 | 0.525847 | 0.506842 | -0.390517 | -0.241209 | -0.409725 | 1.465231 | -0.148455 | -0.354779 | 0.188297 | 0 | 0 |
| 5 | -0.475284 | 0.673200 | -1.077774 | 0.360339 | -0.032017 | 0.910768 | 1.405699 | -0.748908 | 1.465231 | -0.933838 | -0.225835 | -0.922170 | 0 | 0 |
| 6 | 0.163433 | -0.011304 | -1.057752 | 1.128932 | 0.026597 | 1.324397 | -0.060004 | -0.869144 | 1.465231 | -0.902156 | 0.598522 | -1.044263 | 0 | 0 |
| 7 | 0.110500 | 0.178211 | -1.394026 | 0.897710 | -1.243991 | -0.384906 | -0.976009 | -1.391712 | 1.465231 | -1.735560 | -0.662454 | -1.567335 | 0 | 0 |
| 8 | 0.595793 | -0.898440 | 1.924099 | 0.051119 | 0.528166 | 0.377574 | -0.881206 | 1.992593 | -0.169332 | 0.289379 | -1.264549 | -0.986278 | 1 | 0 |
| 9 | -0.017542 | -1.386211 | 0.574605 | -1.268664 | -0.911663 | -1.241512 | -1.036865 | 1.992592 | -1.595580 | 0.436331 | -1.528923 | -1.224807 | 1 | 0 |
| 10 | 0.796579 | -1.216664 | -0.112327 | -0.690935 | 1.077368 | 0.874900 | -0.528379 | 1.992593 | -0.347105 | 1.295264 | 0.069115 | -0.797501 | 1 | 0 |
| 11 | 1.761253 | -0.949116 | -0.297777 | -0.913826 | -0.875567 | -0.968315 | -0.407542 | -0.016419 | -0.556271 | 0.391283 | 0.743737 | 0.460341 | 1 | 0 |
| 12 | 1.055147 | -0.394702 | 1.258031 | -0.517345 | -0.021328 | -0.557321 | 0.669125 | 1.243976 | 0.262937 | 0.337908 | 0.485227 | 2.007205 | 1 | 0 |
| 13 | 1.761253 | -0.435392 | 0.592085 | -0.692391 | 0.535758 | -0.708164 | -0.382176 | 0.125232 | -0.083947 | 0.888176 | 0.994199 | 1.087612 | 1 | 0 |
| 14 | -0.137413 | -0.980041 | -1.297302 | -0.880795 | 1.395884 | -0.901503 | -0.756382 | -0.304071 | -1.277311 | 1.299875 | -1.162641 | -1.274290 | 1 | 0 |
| 15 | 0.017749 | -0.936126 | -1.136240 | -0.862914 | 0.782285 | -0.230220 | -0.734923 | -0.295573 | -1.221528 | 1.299875 | -1.058979 | -1.237947 | 1 | 0 |
| 16 | -0.382429 | -1.386211 | -1.437693 | -1.268664 | 0.481905 | -1.219324 | -1.036865 | 0.284908 | -1.749408 | 1.299875 | -1.532659 | -1.596787 | 1 | 0 |
| 17 | -1.227998 | 1.294887 | 2.200026 | -0.318659 | 2.364886 | -1.078770 | -0.029818 | -1.427905 | -1.667310 | 0.632080 | -1.532496 | 0.393871 | 1 | 0 |
| 18 | -1.076569 | -0.776099 | -0.576736 | -0.929915 | 0.188222 | -1.067980 | 0.156056 | -0.671783 | -0.806376 | 1.299875 | -0.994450 | -0.887100 | 1 | 0 |
| 19 | -1.180746 | 0.385053 | 2.200026 | -0.005979 | 1.302093 | -0.928084 | -0.007355 | -0.793418 | -1.747571 | -0.917240 | -1.528514 | -0.042363 | 1 | 0 |
| 20 | 1.192498 | -0.224351 | -0.206238 | 0.217971 | 0.304072 | 0.949289 | -0.514273 | 1.091683 | 1.465231 | -0.718685 | -0.298094 | -0.264379 | 0 | 0 |
| 21 | 1.213472 | 0.048184 | -0.600330 | 0.007879 | 0.177356 | 1.215438 | -0.394095 | 0.833193 | 1.465231 | -0.381808 | -0.415137 | -0.479222 | 0 | 0 |
| 22 | -0.004572 | -0.052026 | 2.200026 | -0.008377 | 1.967963 | -0.419263 | -0.302302 | 0.046448 | -0.343181 | 1.114245 | -0.245566 | 0.544195 | 1 | 0 |
| 23 | -0.229893 | 0.630662 | 0.484895 | 0.048344 | -0.498767 | 0.662733 | -0.509073 | 0.013777 | 1.465231 | 0.438954 | -0.681106 | -0.745425 | 0 | 0 |
| 24 | 0.217943 | -0.177066 | 0.659322 | -0.134414 | 1.650468 | -0.583176 | -0.559211 | -0.406584 | -0.598204 | 1.299875 | -0.406852 | 0.234437 | 1 | 0 |
| 25 | -0.333597 | 0.972324 | -0.721724 | -0.554449 | -0.493410 | 0.518707 | 0.321268 | -1.070955 | -0.564510 | -0.833869 | 1.695138 | -0.783758 | 0 | 0 |
| 26 | -0.602430 | -0.028610 | -0.826715 | -0.377974 | -0.696055 | -0.182813 | -0.120202 | -0.886178 | -0.584506 | -0.673011 | 1.695138 | -0.599349 | 0 | 0 |
| 27 | -0.251812 | -0.171571 | -0.358821 | -0.244636 | -0.013843 | 0.945000 | 1.038022 | -0.727984 | 0.773167 | -0.048067 | 1.695138 | 0.563056 | 0 | 0 |
| 28 | -0.368678 | 2.234146 | -0.330756 | -0.138877 | -0.559553 | -0.851160 | -0.744847 | -0.884446 | -0.828451 | -1.011890 | -0.568588 | 0.130393 | 0 | 0 |
| 29 | -0.578621 | 2.234146 | -0.562376 | -0.105376 | -0.523903 | -0.825786 | -0.690314 | -0.809436 | -0.870132 | -1.142574 | -0.536734 | 0.859716 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 189 | -0.186741 | -1.165084 | -0.921181 | 1.397816 | -0.045251 | 1.304725 | -0.153535 | 1.038843 | 1.465231 | 0.175798 | 1.209572 | -0.558994 | 0 | 1 |
| 190 | -0.163966 | 0.884601 | 1.657605 | -0.260803 | 1.379747 | -0.732537 | -0.007962 | 1.276498 | -0.419568 | 1.299875 | -0.588765 | 1.807669 | 1 | 1 |
| 191 | -0.254097 | -0.617571 | 0.535718 | -0.297186 | 1.865594 | -0.310035 | 0.916200 | 1.349280 | -0.168599 | 1.299875 | -0.479982 | 1.556484 | 1 | 1 |
| 192 | -1.359431 | 0.228998 | 0.259367 | -1.169606 | 0.516753 | -1.080823 | 1.794240 | -0.257346 | -1.633246 | 0.226711 | -1.071882 | 2.007205 | 1 | 1 |
| 193 | 1.622617 | 2.234146 | -1.127605 | -0.273622 | -1.141311 | 0.831169 | -0.681956 | -1.023930 | 0.097381 | -1.505036 | -0.432936 | -0.703132 | 0 | 1 |
| 194 | -0.467818 | -0.314482 | 0.014154 | 0.025208 | -0.807816 | -0.093075 | 3.055878 | 0.013994 | -0.514838 | -0.698969 | 0.430445 | -0.302544 | 0 | 1 |
| 195 | -1.423661 | -0.599038 | -0.707969 | -1.084898 | -1.206212 | -0.474886 | 1.273960 | -1.605019 | -1.163780 | -1.777082 | 1.695138 | -1.000177 | 0 | 1 |
| 196 | 1.761253 | -0.325612 | 0.357954 | -0.709817 | 1.043699 | -0.927401 | -0.653399 | -0.107407 | -0.553233 | -0.078311 | -0.687092 | -0.080809 | 1 | 1 |
| 197 | 1.761253 | 1.028574 | 1.374947 | 0.116683 | 1.136600 | -0.187955 | -0.195137 | 0.341797 | -0.856237 | 0.366485 | -0.658035 | 0.645642 | 1 | 1 |
| 198 | 1.761253 | 0.050043 | 0.687757 | -0.434894 | 0.705291 | -0.680095 | -0.549325 | 0.816661 | -0.567882 | 0.584770 | -0.302600 | -0.144573 | 1 | 1 |
| 199 | -0.919189 | -0.200271 | -1.032880 | -0.020720 | -1.069924 | -0.649964 | -0.843737 | -0.600356 | 1.465231 | -0.271885 | 0.807204 | -0.901281 | 0 | 1 |
| 200 | -0.988568 | -0.189392 | -1.028379 | 0.102199 | -1.085401 | -0.667213 | -0.580786 | -0.498427 | 1.465231 | -0.287173 | 0.737684 | -0.938325 | 0 | 1 |
| 201 | -0.779270 | 0.427427 | -0.948100 | -0.165441 | -1.033638 | 0.272258 | -0.022274 | -0.198353 | 1.465231 | -0.072695 | 1.561935 | -0.848980 | 0 | 1 |
| 202 | 1.116127 | -1.139042 | 0.168460 | -1.122851 | -0.854043 | 1.067951 | -0.735589 | 0.656822 | 0.343006 | 1.299875 | 0.776152 | -0.247153 | 0 | 1 |
| 203 | 0.762440 | -1.190866 | -0.050517 | -1.116856 | -1.095215 | 0.632777 | -0.797158 | 1.167398 | 1.149430 | 1.299875 | 1.392402 | 0.047424 | 0 | 1 |
| 204 | 0.058411 | -1.128496 | -1.328850 | -1.229911 | -1.074617 | -0.085788 | -0.812628 | 0.326031 | 0.625580 | 1.299875 | 1.290873 | -0.077297 | 0 | 1 |
| 205 | -0.720270 | -0.697672 | -0.720394 | 1.813110 | -0.859595 | -0.861783 | -0.908744 | 1.216314 | 1.411592 | -0.405169 | 1.695138 | -0.439681 | 0 | 1 |
| 206 | -0.364461 | -0.010160 | -1.095500 | 0.914425 | -1.040229 | 0.885108 | -0.998962 | 0.772555 | 1.465231 | -0.838613 | 0.940082 | -0.955178 | 0 | 1 |
| 207 | -0.272236 | -0.196860 | -0.727982 | 0.141418 | -0.904008 | -0.065613 | -0.944991 | 0.795648 | 0.611803 | 0.903563 | 1.695137 | -0.158991 | 0 | 1 |
| 208 | 1.761253 | -0.082523 | -0.122308 | 0.265346 | -0.485445 | 0.500219 | 0.126422 | -0.354469 | 0.139245 | -0.560750 | 1.149004 | 0.354466 | 0 | 1 |
| 209 | 1.573516 | -0.161473 | -0.359909 | 1.241760 | -0.664508 | 1.002463 | -0.076931 | 0.519366 | 1.465231 | -0.646843 | 0.532185 | -0.215564 | 0 | 1 |
| 210 | 1.761253 | 0.417875 | -0.918851 | -0.929813 | -0.982357 | -0.771036 | -0.949451 | -0.679224 | -0.652751 | -1.604058 | -0.786724 | -0.848956 | 0 | 1 |
| 211 | 1.761253 | 0.145550 | 1.710575 | 1.914520 | 1.233461 | 2.474950 | 0.953725 | 1.263177 | 0.830563 | 0.011243 | 0.602765 | -0.024981 | 0 | 1 |
| 212 | 0.470346 | -0.334996 | 2.200026 | 1.858653 | 0.847856 | 1.364055 | 0.061293 | 1.366431 | -0.301856 | -0.501569 | -0.235203 | -0.695720 | 0 | 1 |
| 213 | -0.576457 | -0.914445 | 1.070087 | 0.337357 | 0.306857 | 0.394672 | -0.372356 | -0.450091 | -0.240434 | 0.165141 | 1.695138 | 0.345150 | 0 | 1 |
| 214 | 1.761253 | -0.591066 | -0.690824 | 2.065965 | -0.721336 | -0.340791 | -0.483151 | 0.855908 | 0.529996 | -1.116013 | 0.527710 | -0.050391 | 0 | 1 |
| 215 | 1.490806 | -1.368871 | -1.151960 | 2.846487 | -0.924825 | 0.052478 | -0.970103 | 1.992593 | 0.913611 | -0.452243 | 0.613814 | -1.529552 | 0 | 1 |
| 216 | 0.191801 | -1.348512 | -1.315236 | -0.455163 | -1.244101 | -1.240530 | -1.036865 | -0.531344 | 1.465231 | -1.809822 | -1.350349 | -1.513618 | 0 | 1 |
| 217 | -0.002098 | 2.039653 | -0.752917 | 0.971355 | -0.795869 | 0.431147 | -0.753214 | 0.043687 | 1.465231 | -1.105865 | -0.938582 | -0.984328 | 0 | 1 |
| 218 | -0.098688 | -0.923087 | -0.917548 | 0.312310 | -0.183969 | 0.248120 | -0.545773 | 0.584070 | 0.733937 | -0.697562 | 1.695138 | -0.497086 | 0 | 1 |
219 rows × 14 columns
In [164]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[164]:
<matplotlib.axes._subplots.AxesSubplot at 0x1ef623e6a20>
In [165]:
from IPython.display import display, Markdown, Latex
display(Markdown('## '+companies[5]))
Urban Place¶
ANN¶
In [477]:
X = df_n_ps_std_ch[5]
In [478]:
y = df_n_ps[5]['chosen']
In [479]:
X_train, X_test, y_train, y_test = train_test_split(X, y)
In [480]:
X_train.shape
Out[480]:
(168, 12)
In [170]:
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
In [171]:
activation_vec = ['logistic', 'relu', 'tanh']
max_iter_vec = [10, 20, 50, 75, 100, 200, 300, 400, 500, 1000, 2000]
hidden_layer_sizes_vec = [(10,), (20,), (30,), (10, 10), (20, 20), (30, 30), (20, 10),
(10, 10, 10), (20, 20, 20), (30, 30, 30), (30, 20, 10)]
learning_rate_init_vec = [0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02]
batch_size_vec = [10, 20, 40, 60, 80, 100, 150]
In [172]:
import time
start = time.time() # Devuelve el tiempo actual en segundos desde el 1o de enero de 1970 (punto de referencia)
np.random.seed(1234)
parametros = {'activation': activation_vec,
'max_iter':max_iter_vec,
'hidden_layer_sizes': hidden_layer_sizes_vec,
'learning_rate_init': learning_rate_init_vec#,
#'batch_size': batch_size_vec
}
scoring = {'kappa':make_scorer(cohen_kappa_score), 'accuracy':'accuracy'}
grid = GridSearchCV(mlp, param_grid=parametros, cv=5, scoring=scoring, refit='accuracy', n_jobs=-1, iid=True)
In [173]:
grid.fit(X_train, y_train)
print("Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}".format(
grid.best_params_, grid.best_score_*100, grid.cv_results_['mean_test_kappa'][grid.best_index_]*100))
end = time.time() # Tiempo después de finalizar el entrenamiento del modelo
print("Tiempo total: {0:.2f} minutos".format((end-start)/60))
Los parámetros del mejor modelo fueron {'activation': 'relu', 'hidden_layer_sizes': (20,), 'learning_rate_init': 0.004, 'max_iter': 100}, que permiten obtener un Accuracy de 77.98% y un Kappa del 46.34
Tiempo total: 26.27 minutos
In [481]:
n0=X_train.shape[1]
grid.best_params_['hidden_layer_sizes'] = [20]
### hidden_layer_sizes
ns = []
for i in range (len(grid.best_params_['hidden_layer_sizes'])):
ns.append(grid.best_params_['hidden_layer_sizes'][i])
ns.append(1)
lr =0.004
epochs = 100
In [482]:
input_tensor = Input(shape = (n0,))
In [483]:
hidden_outputs = [input_tensor]
for i in range (len(ns)-1):
hidden_outputs.append(Dense(ns[i], activation = 'tanh')(hidden_outputs[i]))
classification_output = Dense(ns[-1], activation = 'sigmoid')(hidden_outputs[-1])
In [484]:
model = Model([input_tensor], [classification_output])
weights = model.get_weights()
In [485]:
model.summary()
Model: "model_26" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_26 (InputLayer) (None, 12) 0 _________________________________________________________________ dense_82 (Dense) (None, 20) 260 _________________________________________________________________ dense_83 (Dense) (None, 1) 21 ================================================================= Total params: 281 Trainable params: 281 Non-trainable params: 0 _________________________________________________________________
In [486]:
model.set_weights(weights)
adam = keras.optimizers.Adam(lr=lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test),
callbacks=[
keras.callbacks.ReduceLROnPlateau(
monitor='val_accuracy', factor=0.5, patience=10, min_delta=0.01, verbose=1
)
]
)
Train on 168 samples, validate on 57 samples Epoch 1/100 168/168 [==============================] - 0s 1ms/step - loss: 0.8058 - accuracy: 0.5119 - val_loss: 0.6882 - val_accuracy: 0.6140 Epoch 2/100 168/168 [==============================] - 0s 101us/step - loss: 0.7272 - accuracy: 0.5298 - val_loss: 0.6497 - val_accuracy: 0.6316 Epoch 3/100 168/168 [==============================] - 0s 89us/step - loss: 0.6829 - accuracy: 0.5536 - val_loss: 0.6268 - val_accuracy: 0.6316 Epoch 4/100 168/168 [==============================] - 0s 101us/step - loss: 0.6512 - accuracy: 0.6012 - val_loss: 0.5988 - val_accuracy: 0.6667 Epoch 5/100 168/168 [==============================] - 0s 83us/step - loss: 0.6359 - accuracy: 0.6369 - val_loss: 0.5762 - val_accuracy: 0.6491 Epoch 6/100 168/168 [==============================] - 0s 71us/step - loss: 0.6232 - accuracy: 0.6488 - val_loss: 0.5670 - val_accuracy: 0.6667 Epoch 7/100 168/168 [==============================] - 0s 77us/step - loss: 0.6153 - accuracy: 0.6250 - val_loss: 0.5571 - val_accuracy: 0.6667 Epoch 8/100 168/168 [==============================] - 0s 83us/step - loss: 0.6049 - accuracy: 0.6488 - val_loss: 0.5535 - val_accuracy: 0.6491 Epoch 9/100 168/168 [==============================] - 0s 89us/step - loss: 0.5965 - accuracy: 0.6429 - val_loss: 0.5446 - val_accuracy: 0.6842 Epoch 10/100 168/168 [==============================] - 0s 89us/step - loss: 0.5915 - accuracy: 0.6548 - val_loss: 0.5354 - val_accuracy: 0.6667 Epoch 11/100 168/168 [==============================] - 0s 77us/step - loss: 0.5859 - accuracy: 0.6667 - val_loss: 0.5202 - val_accuracy: 0.7193 Epoch 12/100 168/168 [==============================] - 0s 77us/step - loss: 0.5819 - accuracy: 0.6845 - val_loss: 0.5139 - val_accuracy: 0.7193 Epoch 13/100 168/168 [==============================] - 0s 83us/step - loss: 0.5778 - accuracy: 0.6845 - val_loss: 0.5150 - val_accuracy: 0.7193 Epoch 14/100 168/168 [==============================] - 0s 119us/step - loss: 0.5736 - accuracy: 0.6964 - val_loss: 0.5169 - val_accuracy: 0.7018 Epoch 15/100 168/168 [==============================] - 0s 83us/step - loss: 0.5707 - accuracy: 0.7024 - val_loss: 0.5140 - val_accuracy: 0.7193 Epoch 16/100 168/168 [==============================] - 0s 83us/step - loss: 0.5671 - accuracy: 0.7024 - val_loss: 0.5084 - val_accuracy: 0.7193 Epoch 17/100 168/168 [==============================] - 0s 77us/step - loss: 0.5643 - accuracy: 0.7083 - val_loss: 0.5028 - val_accuracy: 0.7368 Epoch 18/100 168/168 [==============================] - 0s 77us/step - loss: 0.5610 - accuracy: 0.7083 - val_loss: 0.4946 - val_accuracy: 0.7544 Epoch 19/100 168/168 [==============================] - 0s 83us/step - loss: 0.5584 - accuracy: 0.7024 - val_loss: 0.4932 - val_accuracy: 0.7544 Epoch 20/100 168/168 [==============================] - 0s 113us/step - loss: 0.5552 - accuracy: 0.7083 - val_loss: 0.4982 - val_accuracy: 0.7368 Epoch 21/100 168/168 [==============================] - 0s 89us/step - loss: 0.5537 - accuracy: 0.7083 - val_loss: 0.5029 - val_accuracy: 0.7368 Epoch 22/100 168/168 [==============================] - 0s 89us/step - loss: 0.5501 - accuracy: 0.7143 - val_loss: 0.4965 - val_accuracy: 0.7544 Epoch 23/100 168/168 [==============================] - 0s 83us/step - loss: 0.5468 - accuracy: 0.7083 - val_loss: 0.4894 - val_accuracy: 0.7544 Epoch 24/100 168/168 [==============================] - 0s 83us/step - loss: 0.5440 - accuracy: 0.7083 - val_loss: 0.4788 - val_accuracy: 0.7544 Epoch 25/100 168/168 [==============================] - 0s 89us/step - loss: 0.5411 - accuracy: 0.7083 - val_loss: 0.4753 - val_accuracy: 0.7544 Epoch 26/100 168/168 [==============================] - 0s 77us/step - loss: 0.5402 - accuracy: 0.7143 - val_loss: 0.4737 - val_accuracy: 0.7544 Epoch 27/100 168/168 [==============================] - 0s 89us/step - loss: 0.5353 - accuracy: 0.7143 - val_loss: 0.4838 - val_accuracy: 0.7368 Epoch 28/100 168/168 [==============================] - 0s 89us/step - loss: 0.5354 - accuracy: 0.7083 - val_loss: 0.5011 - val_accuracy: 0.7193 Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0020000000949949026. Epoch 29/100 168/168 [==============================] - 0s 83us/step - loss: 0.5308 - accuracy: 0.7202 - val_loss: 0.5022 - val_accuracy: 0.7193 Epoch 30/100 168/168 [==============================] - 0s 71us/step - loss: 0.5291 - accuracy: 0.7202 - val_loss: 0.4954 - val_accuracy: 0.7368 Epoch 31/100 168/168 [==============================] - 0s 77us/step - loss: 0.5262 - accuracy: 0.7143 - val_loss: 0.4939 - val_accuracy: 0.7368 Epoch 32/100 168/168 [==============================] - 0s 71us/step - loss: 0.5243 - accuracy: 0.7262 - val_loss: 0.4921 - val_accuracy: 0.7368 Epoch 33/100 168/168 [==============================] - 0s 83us/step - loss: 0.5228 - accuracy: 0.7262 - val_loss: 0.4889 - val_accuracy: 0.7368 Epoch 34/100 168/168 [==============================] - 0s 77us/step - loss: 0.5217 - accuracy: 0.7381 - val_loss: 0.4878 - val_accuracy: 0.7368 Epoch 35/100 168/168 [==============================] - 0s 77us/step - loss: 0.5206 - accuracy: 0.7381 - val_loss: 0.4875 - val_accuracy: 0.7544 Epoch 36/100 168/168 [==============================] - 0s 89us/step - loss: 0.5192 - accuracy: 0.7381 - val_loss: 0.4865 - val_accuracy: 0.7544 Epoch 37/100 168/168 [==============================] - 0s 77us/step - loss: 0.5173 - accuracy: 0.7440 - val_loss: 0.4868 - val_accuracy: 0.7544 Epoch 38/100 168/168 [==============================] - 0s 71us/step - loss: 0.5151 - accuracy: 0.7321 - val_loss: 0.4868 - val_accuracy: 0.7544 Epoch 00038: ReduceLROnPlateau reducing learning rate to 0.0010000000474974513. Epoch 39/100 168/168 [==============================] - 0s 77us/step - loss: 0.5137 - accuracy: 0.7321 - val_loss: 0.4865 - val_accuracy: 0.7368 Epoch 40/100 168/168 [==============================] - 0s 77us/step - loss: 0.5125 - accuracy: 0.7381 - val_loss: 0.4853 - val_accuracy: 0.7544 Epoch 41/100 168/168 [==============================] - 0s 77us/step - loss: 0.5120 - accuracy: 0.7321 - val_loss: 0.4830 - val_accuracy: 0.7544 Epoch 42/100 168/168 [==============================] - 0s 77us/step - loss: 0.5111 - accuracy: 0.7381 - val_loss: 0.4820 - val_accuracy: 0.7544 Epoch 43/100 168/168 [==============================] - 0s 71us/step - loss: 0.5102 - accuracy: 0.7381 - val_loss: 0.4811 - val_accuracy: 0.7544 Epoch 44/100 168/168 [==============================] - 0s 77us/step - loss: 0.5093 - accuracy: 0.7440 - val_loss: 0.4816 - val_accuracy: 0.7544 Epoch 45/100 168/168 [==============================] - 0s 83us/step - loss: 0.5084 - accuracy: 0.7440 - val_loss: 0.4807 - val_accuracy: 0.7544 Epoch 46/100 168/168 [==============================] - 0s 77us/step - loss: 0.5079 - accuracy: 0.7440 - val_loss: 0.4797 - val_accuracy: 0.7544 Epoch 47/100 168/168 [==============================] - 0s 119us/step - loss: 0.5069 - accuracy: 0.7440 - val_loss: 0.4795 - val_accuracy: 0.7544 Epoch 48/100 168/168 [==============================] - 0s 83us/step - loss: 0.5058 - accuracy: 0.7381 - val_loss: 0.4782 - val_accuracy: 0.7544 Epoch 00048: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. Epoch 49/100 168/168 [==============================] - 0s 71us/step - loss: 0.5051 - accuracy: 0.7381 - val_loss: 0.4786 - val_accuracy: 0.7544 Epoch 50/100 168/168 [==============================] - 0s 83us/step - loss: 0.5045 - accuracy: 0.7381 - val_loss: 0.4792 - val_accuracy: 0.7368 Epoch 51/100 168/168 [==============================] - 0s 77us/step - loss: 0.5039 - accuracy: 0.7381 - val_loss: 0.4789 - val_accuracy: 0.7368 Epoch 52/100 168/168 [==============================] - 0s 83us/step - loss: 0.5037 - accuracy: 0.7381 - val_loss: 0.4790 - val_accuracy: 0.7368 Epoch 53/100 168/168 [==============================] - 0s 77us/step - loss: 0.5031 - accuracy: 0.7440 - val_loss: 0.4784 - val_accuracy: 0.7368 Epoch 54/100 168/168 [==============================] - 0s 77us/step - loss: 0.5030 - accuracy: 0.7440 - val_loss: 0.4770 - val_accuracy: 0.7368 Epoch 55/100 168/168 [==============================] - 0s 71us/step - loss: 0.5024 - accuracy: 0.7440 - val_loss: 0.4769 - val_accuracy: 0.7368 Epoch 56/100 168/168 [==============================] - 0s 77us/step - loss: 0.5020 - accuracy: 0.7440 - val_loss: 0.4774 - val_accuracy: 0.7368 Epoch 57/100 168/168 [==============================] - 0s 71us/step - loss: 0.5016 - accuracy: 0.7440 - val_loss: 0.4769 - val_accuracy: 0.7368 Epoch 58/100 168/168 [==============================] - 0s 65us/step - loss: 0.5012 - accuracy: 0.7440 - val_loss: 0.4766 - val_accuracy: 0.7368 Epoch 00058: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628. Epoch 59/100 168/168 [==============================] - 0s 77us/step - loss: 0.5007 - accuracy: 0.7440 - val_loss: 0.4767 - val_accuracy: 0.7368 Epoch 60/100 168/168 [==============================] - 0s 83us/step - loss: 0.5005 - accuracy: 0.7440 - val_loss: 0.4769 - val_accuracy: 0.7368 Epoch 61/100 168/168 [==============================] - 0s 83us/step - loss: 0.5002 - accuracy: 0.7440 - val_loss: 0.4776 - val_accuracy: 0.7368 Epoch 62/100 168/168 [==============================] - 0s 71us/step - loss: 0.5000 - accuracy: 0.7440 - val_loss: 0.4780 - val_accuracy: 0.7368 Epoch 63/100 168/168 [==============================] - 0s 77us/step - loss: 0.4997 - accuracy: 0.7440 - val_loss: 0.4782 - val_accuracy: 0.7193 Epoch 64/100 168/168 [==============================] - 0s 77us/step - loss: 0.4996 - accuracy: 0.7500 - val_loss: 0.4786 - val_accuracy: 0.7193 Epoch 65/100 168/168 [==============================] - 0s 71us/step - loss: 0.4993 - accuracy: 0.7560 - val_loss: 0.4793 - val_accuracy: 0.7193 Epoch 66/100 168/168 [==============================] - 0s 65us/step - loss: 0.4990 - accuracy: 0.7560 - val_loss: 0.4798 - val_accuracy: 0.7193 Epoch 67/100 168/168 [==============================] - 0s 83us/step - loss: 0.4988 - accuracy: 0.7560 - val_loss: 0.4802 - val_accuracy: 0.7193 Epoch 68/100 168/168 [==============================] - 0s 101us/step - loss: 0.4985 - accuracy: 0.7619 - val_loss: 0.4807 - val_accuracy: 0.7193 Epoch 00068: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814. Epoch 69/100 168/168 [==============================] - 0s 95us/step - loss: 0.4983 - accuracy: 0.7560 - val_loss: 0.4809 - val_accuracy: 0.7193 Epoch 70/100 168/168 [==============================] - 0s 89us/step - loss: 0.4982 - accuracy: 0.7560 - val_loss: 0.4812 - val_accuracy: 0.7193 Epoch 71/100 168/168 [==============================] - 0s 77us/step - loss: 0.4981 - accuracy: 0.7560 - val_loss: 0.4815 - val_accuracy: 0.7193 Epoch 72/100 168/168 [==============================] - 0s 83us/step - loss: 0.4979 - accuracy: 0.7560 - val_loss: 0.4817 - val_accuracy: 0.7193 Epoch 73/100 168/168 [==============================] - 0s 101us/step - loss: 0.4979 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 74/100 168/168 [==============================] - 0s 77us/step - loss: 0.4978 - accuracy: 0.7560 - val_loss: 0.4821 - val_accuracy: 0.7193 Epoch 75/100 168/168 [==============================] - 0s 65us/step - loss: 0.4976 - accuracy: 0.7560 - val_loss: 0.4822 - val_accuracy: 0.7193 Epoch 76/100 168/168 [==============================] - 0s 59us/step - loss: 0.4975 - accuracy: 0.7560 - val_loss: 0.4822 - val_accuracy: 0.7193 Epoch 77/100 168/168 [==============================] - 0s 65us/step - loss: 0.4974 - accuracy: 0.7560 - val_loss: 0.4822 - val_accuracy: 0.7193 Epoch 78/100 168/168 [==============================] - 0s 77us/step - loss: 0.4973 - accuracy: 0.7560 - val_loss: 0.4821 - val_accuracy: 0.7193 Epoch 00078: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05. Epoch 79/100 168/168 [==============================] - 0s 65us/step - loss: 0.4972 - accuracy: 0.7560 - val_loss: 0.4820 - val_accuracy: 0.7193 Epoch 80/100 168/168 [==============================] - 0s 65us/step - loss: 0.4971 - accuracy: 0.7560 - val_loss: 0.4821 - val_accuracy: 0.7193 Epoch 81/100 168/168 [==============================] - 0s 65us/step - loss: 0.4971 - accuracy: 0.7560 - val_loss: 0.4820 - val_accuracy: 0.7193 Epoch 82/100 168/168 [==============================] - 0s 65us/step - loss: 0.4970 - accuracy: 0.7560 - val_loss: 0.4820 - val_accuracy: 0.7193 Epoch 83/100 168/168 [==============================] - 0s 65us/step - loss: 0.4970 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 84/100 168/168 [==============================] - 0s 65us/step - loss: 0.4969 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 85/100 168/168 [==============================] - 0s 59us/step - loss: 0.4968 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 86/100 168/168 [==============================] - 0s 65us/step - loss: 0.4968 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 87/100 168/168 [==============================] - 0s 59us/step - loss: 0.4967 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 88/100 168/168 [==============================] - 0s 59us/step - loss: 0.4967 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 00088: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05. Epoch 89/100 168/168 [==============================] - 0s 77us/step - loss: 0.4966 - accuracy: 0.7560 - val_loss: 0.4819 - val_accuracy: 0.7193 Epoch 90/100 168/168 [==============================] - 0s 65us/step - loss: 0.4966 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 91/100 168/168 [==============================] - 0s 65us/step - loss: 0.4966 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 92/100 168/168 [==============================] - 0s 65us/step - loss: 0.4965 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 93/100 168/168 [==============================] - 0s 59us/step - loss: 0.4965 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 94/100 168/168 [==============================] - 0s 59us/step - loss: 0.4965 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 95/100 168/168 [==============================] - 0s 65us/step - loss: 0.4965 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 96/100 168/168 [==============================] - 0s 71us/step - loss: 0.4964 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 97/100 168/168 [==============================] - 0s 59us/step - loss: 0.4964 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 98/100 168/168 [==============================] - 0s 107us/step - loss: 0.4964 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 00098: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05. Epoch 99/100 168/168 [==============================] - 0s 77us/step - loss: 0.4963 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193 Epoch 100/100 168/168 [==============================] - 0s 65us/step - loss: 0.4963 - accuracy: 0.7560 - val_loss: 0.4818 - val_accuracy: 0.7193
In [487]:
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
print(epochs)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
range(0, 100)
In [488]:
test_loss, test_acc = model.evaluate(X_test, y_test)
print("test loss: {}, test accuracy: {}".format(test_loss, test_acc))
57/57 [==============================] - 0s 70us/step test loss: 0.48182071510114166, test accuracy: 0.719298243522644
In [489]:
y_pred = model.predict(X_test)
print("AUC ROC: ",roc_auc_score(y_test, y_pred))
AUC ROC: 0.773015873015873
In [490]:
y_pred = list(map(lambda i: int(i>=0.5), y_pred))
print("Kappa: ",cohen_kappa_score(y_test, y_pred))
Kappa: 0.24378109452736318
KMeans¶
In [66]:
X
Out[66]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 |
255 rows × 13 columns
In [67]:
WSSs = []
for i in range(1,15) :
km = KMeans(n_clusters=i, random_state=0)
km.fit(X)
WSSs.append(km.inertia_)
WSSs
Out[67]:
[3315.0, 2972.7888695817974, 2748.18187155972, 2544.9420084212106, 2413.687059384553, 2278.037996783226, 2213.3487507256823, 2123.4282707474663, 2067.8299633414163, 1977.777252698108, 1956.5229777214513, 1880.0296166971755, 1815.5096049846275, 1785.9955747862728]
In [68]:
plt.figure(figsize=(12,12))
plt.plot(range(1, 15), WSSs)
Out[68]:
[<matplotlib.lines.Line2D at 0x1e82ae84f98>]
K=6
In [69]:
kmeans_ch = KMeans(n_clusters=6, random_state=0, n_init=10)
kmeans_ch.fit(X)
Out[69]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',
random_state=0, tol=0.0001, verbose=0)
In [70]:
kmeans_ch.labels_
Out[70]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [71]:
clusters_ch = kmeans_ch.predict(X)
clusters_ch
Out[71]:
array([4, 2, 2, 2, 0, 1, 1, 0, 1, 4, 4, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 0,
0, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 1, 3, 1, 4, 4, 3, 1, 1, 1, 4, 4,
2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 3, 4, 4, 1, 4, 1, 0, 4, 4, 4, 3, 3,
3, 4, 3, 0, 4, 2, 1, 4, 0, 4, 4, 3, 3, 0, 0, 2, 1, 4, 1, 0, 0, 3,
4, 1, 4, 4, 4, 4, 1, 1, 0, 3, 3, 3, 4, 1, 1, 4, 1, 4, 0, 3, 1, 1,
1, 0, 3, 2, 4, 4, 0, 1, 5, 3, 4, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 2,
4, 4, 0, 0, 2, 2, 2, 4, 4, 4, 2, 2, 2, 3, 0, 4, 1, 3, 3, 3, 3, 3,
4, 3, 1, 0, 0, 0, 1, 1, 0, 2, 2, 0, 3, 0, 1, 0, 0, 0, 0, 1, 3, 2,
4, 4, 1, 2, 4, 3, 2, 3, 4, 4, 4, 1, 2, 3, 3, 0, 2, 2, 2, 1, 0, 1,
4, 2, 0, 4, 3, 3, 2, 3, 5, 5, 2, 3, 3, 2, 0, 3, 0, 2, 4, 2, 1, 2,
1, 3, 4, 4, 4, 1, 0, 1, 2, 2, 3, 0, 4, 2, 2, 4, 0, 2, 2, 2, 1, 3,
4, 4, 2, 2, 2, 4, 0, 0, 2, 2, 2, 2, 2])
In [72]:
X.loc[:,'Cluster'] = clusters_ch
X.loc[:,'chosen'] = list(y)
In [73]:
X
Out[73]:
| mfccfiles_1 | mfccfiles_2 | mfccfiles_3 | mfccfiles_4 | mfccfiles_5 | mfccfiles_6 | mfccfiles_7 | mfccfiles_8 | mfccfiles_9 | mfccfiles_10 | mfccfiles_11 | mfccfiles_12 | mfccfiles_13 | Cluster | chosen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.339415 | 0.847773 | 0.497198 | -0.389310 | 1.225458 | 1.947033 | -0.736267 | 0.492219 | 0.576682 | 1.504697 | -1.796460 | 0.724954 | 0.958600 | 4 | 0 |
| 1 | 0.587658 | -1.195426 | 0.636375 | 0.199876 | 0.765321 | 0.061181 | 0.379367 | -0.440867 | 0.232893 | 1.339920 | 0.110001 | 0.807525 | 0.815678 | 2 | 0 |
| 2 | 1.465595 | -2.307943 | 0.354567 | -0.058273 | -1.298853 | -0.811453 | -1.551580 | -3.934320 | -1.079432 | 2.546130 | 1.421407 | 0.639359 | 0.199094 | 2 | 0 |
| 3 | 0.749403 | -1.690498 | -0.125200 | -1.016135 | 0.825845 | 0.271444 | -0.104786 | -0.992141 | 0.049182 | 1.425948 | -0.343269 | -0.789558 | -0.411898 | 2 | 0 |
| 4 | -0.280577 | 0.393332 | 0.744917 | 2.411400 | -0.777421 | -0.420018 | 1.258355 | -1.544565 | -0.498071 | 0.421527 | -0.632908 | -0.056846 | -0.072348 | 0 | 0 |
| 5 | -0.158690 | 0.404891 | -0.147920 | -0.299241 | -0.786974 | 0.697216 | 0.290501 | 0.019739 | -1.468086 | -0.346174 | -0.086965 | 0.026492 | 1.019512 | 1 | 0 |
| 6 | 1.646777 | 0.772744 | -1.425228 | -0.562610 | -1.556076 | 0.533289 | -0.404271 | 1.676958 | 0.979516 | 0.415548 | 0.544719 | 0.433332 | 0.204271 | 1 | 0 |
| 7 | 1.124970 | 0.506236 | 0.738993 | 1.984485 | -0.928706 | -0.494097 | -0.707105 | -0.494778 | -1.642929 | 0.207467 | 0.181382 | 2.431721 | 0.848697 | 0 | 0 |
| 8 | 0.920059 | 1.438862 | -2.048354 | 1.503567 | -2.801303 | 0.567132 | -0.745441 | 0.569519 | 0.130917 | 1.965436 | -0.034797 | 1.164878 | 0.074074 | 1 | 0 |
| 9 | 0.182544 | 0.310622 | 0.067722 | 0.870138 | 0.168366 | 0.682045 | -0.191296 | -0.144962 | -0.630020 | -0.284032 | -0.315301 | 0.344841 | 0.495167 | 4 | 0 |
| 10 | 0.168663 | 0.389450 | 0.034360 | 1.213392 | 0.248437 | 0.870618 | -0.460824 | -0.174734 | -0.710502 | -0.228408 | -0.265153 | 0.349416 | 0.584114 | 4 | 0 |
| 11 | 0.153010 | -0.118336 | 0.639531 | 1.504522 | 0.937909 | 0.356048 | -0.089987 | -0.628522 | 0.064203 | 0.966049 | 0.403915 | -0.943626 | 0.173874 | 4 | 0 |
| 12 | 0.132578 | 0.261966 | -2.871493 | -3.398160 | -0.256458 | 1.596532 | -0.358711 | 0.175955 | -0.499075 | 0.949085 | 2.235525 | -0.197712 | -0.272366 | 1 | 0 |
| 13 | 1.094629 | 0.885150 | -1.130672 | -0.083270 | 0.672482 | 0.750453 | -0.863949 | 0.140540 | 0.423312 | -0.305155 | -0.424905 | 0.318660 | 0.885900 | 4 | 0 |
| 14 | 0.771472 | 0.364448 | -0.454696 | 0.434253 | 0.912699 | 0.745924 | -0.073390 | -0.406473 | 0.450765 | 0.323180 | -0.458826 | -0.132295 | 0.495454 | 4 | 0 |
| 15 | 0.677561 | 0.166795 | 0.746471 | 0.075191 | 0.867924 | -1.621678 | 0.771146 | -0.067286 | 0.557998 | -0.093593 | 0.020233 | -0.800013 | -0.629188 | 3 | 0 |
| 16 | -0.032353 | 1.227345 | -0.188580 | 0.927210 | 0.016663 | 1.001867 | -0.473811 | 0.782387 | 1.542760 | -0.345478 | -0.838104 | -0.439443 | 1.179204 | 4 | 0 |
| 17 | 0.459031 | 1.258961 | -0.329412 | 1.391790 | -0.208888 | 1.059241 | -1.245671 | 0.619153 | 0.245780 | 0.644548 | -0.602629 | -0.928581 | 0.739885 | 4 | 0 |
| 18 | -0.359172 | 0.051214 | -0.603962 | 0.778896 | 1.630471 | 1.802477 | 1.486205 | -0.140738 | -0.894366 | 0.736624 | 2.114721 | 1.078175 | -0.965785 | 4 | 0 |
| 19 | 0.209859 | -0.615399 | -0.676895 | 0.735655 | 0.805509 | -0.696793 | 1.073068 | 0.240429 | -0.205934 | -0.759693 | 0.672843 | 0.569482 | -0.455391 | 4 | 0 |
| 20 | 0.127381 | -0.265099 | -0.258801 | -0.127568 | 0.649447 | 0.244473 | 1.897421 | -0.344616 | -0.593159 | 0.065147 | 1.787607 | 1.219355 | -0.171813 | 4 | 0 |
| 21 | 1.222717 | 0.409860 | 1.311826 | 0.703873 | 0.322062 | 0.305461 | -0.522644 | -0.750833 | 0.001767 | 0.017953 | 0.254329 | -0.227762 | -0.614790 | 0 | 0 |
| 22 | 1.173352 | 0.490500 | 0.742825 | -0.028159 | -0.272396 | -0.502733 | -0.759443 | -1.031924 | -0.157975 | 0.075659 | 0.604220 | 0.143298 | -0.001849 | 0 | 0 |
| 23 | 1.069960 | 0.858822 | -0.795544 | 0.076688 | 0.851875 | 0.735014 | -0.758779 | 0.065595 | 0.532667 | -0.391858 | -0.497019 | 0.240822 | 0.848126 | 4 | 0 |
| 24 | 0.581377 | -0.804045 | 0.399887 | 1.535671 | 0.245878 | 0.904192 | -0.233991 | -0.925983 | 0.212280 | 0.499535 | -0.024926 | -0.925999 | 1.294925 | 4 | 0 |
| 25 | 0.161110 | 0.025075 | 0.716318 | 1.532230 | 0.889883 | 0.353167 | -0.058787 | -0.593046 | 0.093773 | 0.927085 | 0.199691 | -0.979872 | 0.232850 | 4 | 0 |
| 26 | 0.431443 | 0.442713 | 0.259120 | 0.045533 | 0.102675 | 0.367606 | 0.054320 | 0.942924 | 0.180609 | 0.550983 | 0.265291 | 0.321252 | -0.830969 | 4 | 0 |
| 27 | 0.344525 | -1.140315 | -0.725453 | -0.547965 | 0.449924 | 0.303904 | 1.053624 | 1.051712 | 0.509322 | 0.181611 | -0.519979 | -1.134490 | -1.439105 | 3 | 0 |
| 28 | -0.041565 | 0.671274 | 0.195143 | 0.247294 | 0.531620 | 1.050124 | 0.311358 | 0.988161 | -0.198869 | 0.387795 | 1.757366 | 1.351684 | 0.194840 | 4 | 0 |
| 29 | 0.417845 | -1.134173 | -0.760709 | -0.605264 | 0.077464 | 0.533333 | 1.104524 | 2.124971 | 0.083548 | 0.801730 | 0.092534 | -1.281628 | -1.468782 | 3 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 225 | 1.532114 | -1.060006 | -0.434145 | -0.999435 | -1.259462 | 0.039140 | -0.802013 | -0.655286 | 0.714448 | 1.005958 | -0.086372 | 0.537392 | 0.054440 | 1 | 1 |
| 226 | -0.942320 | 1.172080 | 0.506725 | -0.230675 | -0.104635 | 0.898742 | -1.107001 | -1.182148 | -0.940991 | 0.232366 | 1.778224 | 0.975251 | 1.731084 | 0 | 1 |
| 227 | 1.421974 | 0.631029 | -0.563813 | -0.694595 | -0.673270 | 0.929022 | 0.476907 | -1.025173 | -0.813644 | -0.060006 | -0.738730 | -0.558099 | 0.057654 | 1 | 1 |
| 228 | -1.473385 | -0.806223 | 1.849423 | -1.252541 | 0.941013 | -0.872947 | -1.812392 | -0.242718 | -0.097212 | -0.510500 | -0.232195 | -0.546399 | 0.945530 | 2 | 1 |
| 229 | -1.135926 | -0.772372 | 1.164844 | -1.022517 | 0.630202 | -0.496999 | -1.101656 | -0.168921 | -0.295159 | -0.587401 | 0.369033 | -0.266325 | 0.604469 | 2 | 1 |
| 230 | -1.085049 | 0.879566 | 0.442593 | 0.128917 | 0.393498 | 0.531555 | 0.392194 | 1.418515 | 0.891015 | -0.348926 | -0.756201 | -0.838584 | -0.015971 | 3 | 1 |
| 231 | -0.352258 | 0.556982 | 0.530520 | 0.443818 | 0.300921 | 0.032128 | -0.797384 | -0.573532 | 0.398084 | 0.328875 | -0.274964 | -1.300920 | 0.254456 | 0 | 1 |
| 232 | -1.190363 | 0.797356 | 0.758472 | 0.587917 | 0.890540 | 0.471925 | 0.105793 | 0.680721 | 0.230834 | -0.150709 | -0.816744 | -0.470618 | 0.371198 | 4 | 1 |
| 233 | -0.651003 | -0.586618 | 1.326854 | -0.451354 | 0.507113 | 0.165474 | -0.919675 | -0.448249 | -1.310940 | -1.372737 | 0.406029 | -1.414627 | -0.434858 | 2 | 1 |
| 234 | -1.459511 | -0.516281 | 1.631699 | -1.141842 | 0.584621 | -0.458541 | -1.428877 | -0.934556 | -0.216455 | -0.049794 | 0.095580 | 0.387068 | 0.693730 | 2 | 1 |
| 235 | -0.726984 | 0.702447 | 0.798069 | -0.320660 | 0.530902 | 1.019988 | 0.144995 | 0.207847 | 0.039592 | 0.220761 | 0.762941 | 0.575034 | 0.671517 | 4 | 1 |
| 236 | -0.300986 | -0.404923 | 0.715406 | 0.245380 | -0.427936 | -0.334843 | -0.228084 | -0.330898 | -0.674327 | 0.199560 | 0.827455 | 0.016433 | 0.866789 | 0 | 1 |
| 237 | -0.736244 | 0.088611 | 0.910051 | 0.437100 | 0.258256 | 0.363828 | -0.415290 | -0.717445 | -0.012727 | 0.436925 | -0.786954 | -1.217376 | 0.352825 | 2 | 1 |
| 238 | 0.610473 | -2.664315 | 1.303652 | -2.022376 | 1.500032 | -1.280926 | -1.249533 | 0.432111 | -0.768558 | 0.291156 | -0.092312 | 0.053770 | -0.401166 | 2 | 1 |
| 239 | -2.045424 | -2.954642 | 0.302601 | -0.868092 | -1.038134 | -1.230777 | 0.514329 | 0.057591 | -1.023895 | 0.275395 | -1.450282 | 0.386242 | 0.318763 | 2 | 1 |
| 240 | 0.329793 | -1.367570 | -1.454329 | -0.207924 | -0.723609 | -0.149025 | -0.085298 | -0.011595 | -0.240239 | -0.009120 | -0.325229 | -0.025722 | 0.114182 | 1 | 1 |
| 241 | -1.919591 | 1.382172 | -0.134161 | 0.837967 | -0.687780 | 0.944303 | -0.258652 | -0.742178 | 0.386031 | -1.178099 | -1.843543 | -0.710556 | -0.318561 | 3 | 1 |
| 242 | -2.087669 | 1.400006 | -0.494964 | 0.451717 | -0.759188 | 0.736625 | 0.133121 | -0.196031 | 1.121231 | 0.474128 | -0.345937 | -0.409324 | -0.442069 | 4 | 1 |
| 243 | -2.131652 | 0.439305 | -0.612226 | 0.854126 | -0.494550 | 0.825299 | 0.301373 | -0.018964 | 0.690556 | -0.078762 | -0.709495 | -0.075857 | -0.418656 | 4 | 1 |
| 244 | -1.611989 | -0.756403 | -0.410917 | 1.075909 | 0.297336 | -1.317576 | 1.115011 | -0.467065 | -0.768378 | 1.615499 | 1.611125 | -1.018782 | -1.798744 | 2 | 1 |
| 245 | -0.142010 | 0.000190 | -0.063461 | -0.506353 | -0.386942 | -0.256144 | 0.270621 | -1.497417 | 0.507892 | 0.456828 | -0.431169 | -0.978417 | 0.015849 | 2 | 1 |
| 246 | -1.263975 | -1.168117 | -1.396090 | -0.312016 | 1.862268 | 1.400290 | 0.646060 | -0.686864 | 0.418524 | -0.069926 | -0.653856 | -0.853617 | -0.106814 | 2 | 1 |
| 247 | -0.507700 | 0.899825 | 1.510153 | 1.083642 | 2.081451 | 0.589016 | 0.901321 | 0.658808 | 0.152596 | 0.176442 | -0.447633 | 0.287838 | 0.650479 | 4 | 1 |
| 248 | -0.159768 | 0.518093 | 2.197018 | 0.698491 | 0.476336 | -2.014255 | -1.614667 | -0.397282 | -1.781932 | -0.208894 | 1.650551 | -0.771436 | -0.987237 | 0 | 1 |
| 249 | -1.037899 | 1.016712 | 2.774230 | 0.665468 | -0.385673 | 0.587263 | -0.121609 | -0.331379 | 0.622484 | -0.387131 | -0.276584 | 0.218207 | 1.689216 | 0 | 1 |
| 250 | -0.526923 | -1.169944 | 0.474875 | -0.789231 | 0.369827 | -0.537003 | -1.089843 | -0.173366 | -0.023237 | -0.142334 | 0.740065 | 0.813114 | 0.872556 | 2 | 1 |
| 251 | -0.770856 | -1.024349 | -0.019140 | -0.097521 | 0.092703 | 0.369242 | -0.273901 | 0.190740 | -0.074032 | 0.113055 | 0.140291 | -0.696275 | 0.166679 | 2 | 1 |
| 252 | -0.905458 | -0.790575 | 0.206164 | -0.723816 | -0.444860 | 0.107833 | -0.734514 | -0.533865 | -0.634334 | 0.320526 | 0.088428 | -0.348210 | 0.347201 | 2 | 1 |
| 253 | -1.378235 | -0.338405 | 0.016815 | -0.394563 | 0.034043 | 1.023865 | -0.303960 | -1.316121 | 0.198697 | 0.670577 | 0.809574 | 0.580565 | 0.056004 | 2 | 1 |
| 254 | -0.199959 | -2.035812 | -0.904507 | -1.511975 | -0.437843 | 0.262972 | -1.943788 | -1.963300 | -2.256227 | 0.354369 | -0.039829 | 0.882325 | 0.139307 | 2 | 1 |
255 rows × 15 columns
In [74]:
stacked = X.groupby(['chosen','Cluster']).size().reset_index()
pivot_df = stacked.pivot(index='Cluster', columns='chosen', values=0)
pivot_df.loc[:,[0,1]].plot.bar(stacked=True, figsize=(10,7))
Out[74]:
<matplotlib.axes._subplots.AxesSubplot at 0x1e82aec1940>
In [ ]:
In [ ]:
In [ ]: